workshop “mt roadmap and masterplan” and results · d6.8.1 workshop “mt roadmap and...

This document is part of the Coordination and Support Action “Preparation and Launch of a Large-scale Action for Quality Translation Technology (QTLaunchPad)”, funded by the 7th Framework Programme of the European Commission through the contract 296347.

Workshop “MT RoadMap and

MasterPlan” and Results

Author(s): Aljoscha Burchardt, Stephen Doherty, Josef van Genabith, Arle Lommel, Hans Uszkoreit (eds.)

Dissemination Level: Public

Date: 20.08.2013

Preparation and Launch of a Large-scale Action for Quality Translation Technology D6.8.1 Workshop “MT RoadMap and MasterPlan” and Results

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Grant agreement no. 296347 Project acronym QTLaunchPad Project full title Preparation and Launch of a Large-scale Action for Quality Transla-

tion Technology Funding scheme Coordination and Support Action Coordinator Prof. Hans Uszkoreit (DFKI) Start date, duration 1 July 2012, 24 months Distribution Public Contractual date of delivery June 2013 Actual date of delivery August 2013 Deliverable number 6.8.1 Deliverable title Workshop “MT RoadMap and MasterPlan” and Results Type Report Status and version Number of pages Contributing partners USFD WP leader DFKI Task leader DFKI Authors Aljoscha Burchardt, Arle Lommel, Maja Popovic EC project officer Aleksandra Wesolowska The partners in QTLaunchPad are:

Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Germany

Dublin City University (DCU), Ireland Institute for Language and Speech Processing, R.C. “Athena”

(ILSP/ATHENA RC), Greece The University of Sheffield (USFD), United Kingdom

For copies of reports, updates on project activities and other QTLaunchPad-related information, con-tact:

DFKI GmbH QTLaunchPad Dr. Aljoscha Burchardt [email protected] Alt-Moabit 91c Phone: +49 (30) 23895-1838 10559 Berlin, Germany Fax: +49 (30) 23895-1810

Copies of reports and other material can also be accessed via http://www.qt21.eu/launchpad

© 2013, The Individual Authors No part of this document may be reproduced or transmitted in any form, or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, with-out permission from the copyright owner.


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Table of Contents 1 Preamble ........................................................................................................................... 4 2 Introduction: Planning of QT21 .......................................................................................... 4 3 MT RoadMap 2014–2020 .................................................................................................. 5

3.1 Key Performance Impact Factors for MT .................................................................... 6 3.2 The Road Ahead ......................................................................................................... 8

4 MasterPlan for QT21 ....................................................................................................... 10 4.1 Background: RIASes ................................................................................................. 10 4.2 Description of the RIASes ......................................................................................... 11 4.3 Leading-edge Research Plan .................................................................................... 19 4.4 Project Structure of QT21 .......................................................................................... 23 4.5 Communications Plan ............................................................................................... 26 4.6 Resource Acquisition Plan ........................................................................................ 28

5 Appendix: List of Planning Panel Members ..................................................................... 31 6 Appendix: META-NET RoadMap “Translingual Cloud” (Version: March 2013) ............... 32


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1 Preamble This internal planning document is a living document representing the current state of plan-ning activities for a big quality translation initiative QT21. It has been provided and discussed by members of the QT21 Planning Panel (see Appendix). This document will be updated and modified to adjust to future developments and to improve its relevance to funding oppor-tunities. NOTE: This document has considerable overlap with Deliverable 6.3.1 (Planning Meeting Results) and incorporates substantial portions of that document. This overlap is intentional since much of the content here is derived from meetings of the Planning Panel.

2 Introduction: Planning of QT21 The central objective of QTLaunchPad is the preparation of a large-scale research and inno-vation action, Quality Translation Technology for the 21st Century – A European Initiative (QT21). QT21 comprises:

• A new research paradigm based on the analytical investigation and elimination of barriers to translation quality;

• An unprecedented collaboration of the best MT research actors in Europe; • A broad and sophisticated infrastructure of resources (data and tools); • Close integration of human translation professionals into the research process; • Close cooperation with large translation customers; • Integration of the wealth of results of computational linguistics research.

QT21 will be dedicated to the systematic attack of quality barriers through competitive re-search (see Section 4.4 for more details on the notion of “competitive research”). An im-portant novel aspect of this new way of approaching MT research will be the cooperation with motivated professional translators through strong involvement of the European transla-tion industry including LSPs and buyers of translation, making the best use of translators’ perspective and expertise. The “one size fits all” approach currently employed by all freely available online translation services fails to deliver translation quality sufficient for many common specialised types of input (e.g., technical text or multimedia content). Valid and reli-able translation quality assessment, based on the multi-dimensional metric system devel-oped in the QT Launchpad project, is a key element of the infrastructure of QT21. One im-portant strategic factor for achieving quality translations in the large QT action is to concen-trate on specific domains and text types (genres) by systematically building up, pre-processing and exploiting domain- and text type-specific corpora. Key technologies needed for the gathering of these highly specific data still need to be developed. Accordingly, QT21 will concentrate on a few domains and text types as described in the respective use cases (Research Innovation Application Scenarios or RIASes) below. Including the actual users of the services to be developed will allow the individual projects be able to involve them more closely, to adapt technologies and methods to their needs, and to make use of their vast repositories of existing data sets (e.g., translation memory databases). The QT21 Planning Panel is a board of stakeholders who have established themselves as thought leaders and would qualify as potential participants of QT21. The planning process, which is driven and coordinated by the QTLaunchPad Extended Steering Board, consists of several phases. The first meetings of the Planning Panel took place:


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• September 13/14, 2012 at DFKI in Berlin; • October 25, 2012 at Hotel Crowne Plaza in Wiesbaden (co-located with

tekom/TCworld events); • March 13, 2013 at FAO Headquarters in Rome (co-located with Multilingual Web

Workshop). At these meetings, a large portion of the time was dedicated to the presentation and discus-sion of the RIASes and the research strategies needed for them. In the current planning phase, work was delegated to working groups as outlined in the DoW. The groups have communicated via email/document sharing and also met in virtual meetings via online and telephone conferences. The common result has been given to the planning panel members for feedback. It is documented in this report. The main goal of the planning phase reported here was to turn the RIASes described below into the MasterPlan for QT21, including a leading-edge research plan and the roadmap for the field of MT. The roadmap for MT presented in this document is a continuation and con-cretisation of the roadmap for the whole field of language technologies as defined in the ME-TA-NET Strategic Research Agenda for LT in Europe that was presented publicly in January 2013.1

3 MT RoadMap 2014–2020 Data-driven models (predominately statistical machine translation or SMT) are often consid-ered the current state-of-the-art in machine translation. SMT models are attractive: they pro-vide generic technologies that (in principle) are language agnostic and (with suitable refine-ments and pre-processing) can be applied successfully to many languages; they are con-ceptually simple, highly scalable and robust and underpin many of today’s commercial and for-free web-based applications; they are easy to train and—given suitable training data and computing resources—can be up and running for new languages and domains within a mat-ter of hours or within a couple of days. At the same time, SMT technologies are reaching a point of diminishing returns that can be seen as a current performance plateau. The level of abstraction over the training data in cur-rent SMT systems consists largely of n-grams and combinations of n-grams; despite recent advances using structural information (e.g. syntactic trees), reordering and long-range de-pendencies in language data are still not always adequately modelled. Further substantial improvements in translation quality usually require training data sets to be orders of magni-tude larger or for users to invest significant resources in “cleaning” data or adding language-specific rules to “normalize” linguistic input. As a result, a boost in progress is needed if qual-ity is to improve over current levels. In addition to SMT technologies there are a number of carefully hand-crafted commercial Rule-Based MT (RBMT) systems on the market. Often these have been developed over many years and versions of them been carefully tuned to particular customer application 1 http://www.meta-net.eu/sra-en


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scenarios, generally with good results. The time and effort required to develop such systems, however, is often prohibitive and, to the best of our knowledge, very few (if any) efforts are currently under way to build full-scale RBMT systems for new language pairs. Research ac-tivity instead has shifted towards (1) learning/deriving statistical RBMT resources (analysis, transfer and generation) from (usually) automatically annotated (e.g., parsed) bitext data, and (2) to “guaranteed correct” translation approaches that use templates and rules to cap-ture translations for constrained sub-languages (controlled languages). The following sections outline a roadmap for developments in machine translation technolo-gies in 2014–2020 timeframe that builds upon the “Translingual Cloud” roadmap developed by META-NET for the Strategic Research Agenda. The META-NET roadmap (see Appendix) foresees the development of HQMT for phase 2 (2015-2017) and deployment of HQMT for phase 3 (2018-2020). With this proposal, we hope to accelerate this plan a little. We broadly define the RoadMap in terms of four Key Performance Impact Factors (KPIFs) that we expect to determine core improvements in translation technologies. The KPIFs are:

• Disruptive New MT Approaches; • New Technologies for Proven MT Approaches; • Innovative Applications; • More and/or Better Data.

These are described in more detail below. We expect these KPIFs to act on multiple broad (and not-necessarily exclusive) strands in translation technologies and their applications, such as the following:

1. High-Quality (often outbound) Translation; 2. Gisting (often inbound, web-based content); 3. Mobile Translation of Spoken Language (Personal Digital Assistant); 4. Multi-Party Dialogue Translation; 5. Contextualised Text and Speech Translation in Multi-Modal and Multi-Media Applica-

tions. Numbers 1 and 2 focus on textual data, 3 and 4 on spoken language, and number 5 inte-grates text, speech, modalities, and contextual information. In this document, especially in the sections on the Leading-Edge Research Plan and the QT21 MasterPlan, we focus on High Quality Translation.

3.1 Key Performance Impact Factors for MT To a first approximation, the key performance impact factors (KPIF) can be characterised as described below.

3.1.1 Disruptive New Approaches Disruptive new approaches and technologies that lead to breakthroughs in translation quality can come in three (non-exclusive) main forms:

i. Completely new MT paradigms are paradigms that are different from the architec-ture and processing models of current established MT approaches, be they SMT- or RBMT- based or inherently hybrid. Such paradigms are difficult to predict, and it is not always completely clear how “new” a paradigm actually is and how much it is an evolution of previous technologies. An example of a possible new paradigm may be


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MT based on deep neural nets; a completely new goal might be translation of non-literal language.

ii. Substantial model improvements are substantial improvements to key compo-nents of today’s translation models and engines such as better abstraction over train-ing data, clustering, the use of semantic information (both linguistic and Linked Open Data (LOD)), new ways of capturing reordering and long-range phenomena, im-proved automatic evaluation measures driving parameter optimisation and compo-nent integration, novel and improved alignment models, better use of data in the long tail, etc.

iii. Working with non-linguistic context and multi-modal content. One of the limita-tions for automatic translation today is that it cannot leverage non-textual information that could help it disambiguate texts or provide improved translation. As data analysis of non-textual data improves, the results could be used to help provide translations of images, video, and audio files, and to use non-textual knowledge to improve selec-tion of domain and engines to improve translation quality.

3.1.2 New Technologies for Proven MT Approaches2 These involve new technologies (but also optimisation of existing technologies) for the opti-mal embedding and integration of existing MT approaches into workflows with the potential of substantial productivity increases without necessarily increasing the translation quality of the base translation technology. Examples include a diverse range of technologies such as:

• Better pre- and post-editing of MT data; • Human-centric MT developed inclusively with users at every stage of development; • Ergonomic and intelligent translator’s cockpits; • Incremental/on-the-fly retraining of MT models using the output of human post-editing

efforts to avoid making the same mistake twice; • Automatic error detection; • Integrating (automatic) quality estimation and (human) quality assessment into MT

workflows; • Domain adaptation; • Handling potentially noisy user-generated content; • Multi-engine and combination systems; • New economies of intellectual work involving extreme division of labour through

complex distributed teams and individuals including translation, language, and (mon-olingual) domain experts in a flexible crowdsourcing-inspired workflow scenario.

These lines of research and development would be based on the results of successful pro-jects such as MATECAT, EuroMatrixPlus, CASMACAT, EU-Bridge, etc.

3.1.3 Innovative Applications Innovation cuts across both New Disruptive Technologies and New Technologies for Proven MT approaches: in the first case, innovation identifies and captures the potential for early

2 Note that such technologies can also further increase the productivity gains obtained from increased translation quality obtained by new disruptive MT technologies.


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exploitation of a disruptive technology for new applications; in the second case it establishes new connections between potentially fruitful ideas and applications for existing technologies. Innovate applications thus accelerate the uptake of MT and help serve new markets and increase productivity. Examples include new services and products (e.g., related to the In-ternet of Things), but also “in vivo” experimentation platforms where disruptive technologies and/or new technologies for proven MT approaches are exposed to large-scale live testing and on-line and real time evaluation of updates and improvements. Innovation crucially relies on a favourable climate for commercialisation, with rapid and flexible financing mechanisms and shorter lab-to-product cycles.

3.1.4 More and/or Better Data On August 5th, 2013 the Web was estimated to contain around 40 billion indexed pages.3 Most of these feature natural language as their primary content and/or metadata. At the same time, the Web is developing into a strongly multilingual space, with English, once clearly the dominant web language, currently accounting for only 55.4% of websites.4 Multi-lingual processing capabilities are a key component of Big Data: the storage, curation, search, analysis, and visualization of exabytes of data. At the same time, established and emerging web technologies, including linked open data, will grow to provide important new knowledge sources for machine translation. Data is the lifeblood of statistical and machine learning based approaches to machine trans-lation. Currently bitext data (e.g., Europarl, JRC Aquis, OPUS) are available in sufficient vol-ume and quality for a limited number of languages and domains. By contrast, monolingual data (for language modelling) are more readily available in much larger quantities and more diverse domains. We expect the situation for bitext data to improve significantly over the next five years (and beyond): steadily increasing levels of globalised trade, commerce, tourism, education, entertainment, social and NGO activities result in increasing levels of translation and resulting bitexts for more language pairs and domains. Notwithstanding these develop-ments, the combinatorics of possible source and target language pairs together with (a po-tentially large) number of domains (and registers) means that not every case will be covered by sufficient relevant training data. Therefore pivot-based translation techniques, techniques for extracting training material from comparable (rather than parallel) corpora and techniques for automatically retrieving and selecting supplementary training data will remain important in the foreseeable future. Note that these four KPIF areas are not exclusive and that all types of synergies can be ex-pected, e.g., the availability of new types of data can enable innovative applications, etc.

3.2 The Road Ahead Developing and projecting a roadmap for a field as dynamic and rapidly changing as Ma-chine Translation is extremely difficult. Accordingly, the further we go into the future the more open and less determined the roadmap is. Our RoadMap extends the META-NET “Translingual Cloud” roadmap (found in the Appendix). While the META-NET roadmap is structured according to main application areas, the extension presented here is determined

3 See http://www.worldwidewebsize.com. Accessed August 5th 2013. 4 See http://w3techs.com/technologies/overview/content_language/all. Accessed August 5th 2013.


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by the KPIFs, which then inform the QT21 MasterPlan (Section 4). It adapts the KPIFs and the Leading-Edge Research Plan from META-NET to the particulars of QT21. The RoadMap carves up the period from 2014 to 2020 into two three-year phases: 2014–2017 and 2017–2020. This RoadMap is summarized in Table 1:

QT21: RoadMap for HQMT (Extending META-NET RoadMap “Translingual Cloud”)

Key Performance Impact Factors

2014-2017 2017-2020

Disruptive new Ap-proaches

Success indicators for HQMT: • 50% quality and productivity improvements

• Confluence and synergies be-tween models, technologies, innovation and data

• Scalability • In-vivo models

for continuous improvements

New technologies for proven ap-proaches

Success indicators for HQMT: • 30% productivity improvements coupled with exist-

ing MT technologies • 80% quality and productivity improvements coupled

with Disruptive Models

Innovative Applica-tions

Success indicators for HQMT: • Five new innovative applications

More and/or Better Data

Success indicators for HQMT: • >100M segments of bitext training data for top 50

language pairs • >10M segments of bitext training data for top 50-

500 language pairs • >1M segments for top 500-2000 language pairs

Table 1: Extended RoadMap for HQMT For HQMT, we formulate the targeted quality and performance improvements schematically with the help of the ratio between the three levels translation quality we propose with some-what arbitrary labels: “good”, “editable”, and “bad” (though potentially helpful for gisting pur-poses). Naturally, the ratio of these labels highly depends on factors such as the languages involved, task, domain, source quality, available resources, etc. For the given purpose, we start from the following ratio: After the first phase (2014–2017), we target a 50% improvement in the good and editable parts using disruptive new approaches: Or using new technologies for proven approaches, where we expect a 30% productivity improvement, which would mainly fall into the “editable” part:

editable good bad

good editable bad

good editable bad


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4 MasterPlan for QT21 The QT21 action will provide a dedicated and systematic attack on quality barriers through cutting edge competitive research and innovation linked with strong community and stake-holder engagement. The novelty of this approach to MT research and development will con-sist of our growing network of motivated translation and localisation professionals coupled with an established relationship with the European translation industry. A key strategic factor for achieving high-quality translations in the QT21 action is to concentrate on specific do-mains and types of content by systematically collating, pre-processing and exploiting do-main- and content-specific corpora effectively. The missing key technologies needed for the-se highly specific processes and data will be developed. This is a contrast to the “one size fits all” approach employed by all publicly available online translation services, which cannot deliver high-quality translation for many specialised areas. QT21 will therefore concentrate on the selected domains and content types as described in the RIASes (see 4.1 below). Owing to the inclusion of actual users as partners in the ser-vices to be developed, individual projects will be able to involve them more closely and con-sistently in order to adapt technologies and methods to meet their needs. At the same time this allows for the sharing of valuable user feedback and expertise and allows access to vast repositories of existing data sets. This section describes the current state of planning activi-ties in the following:

• Background & Description of the RIASes; • Leading-edge Research Plan for needed and foreseen horizontal forefront research; • Project Architecture for QT21; • Communications Plan for QTLaunchPad and later QT21; • Resource Acquisition Plan: Initial plan for the acquisition of resources for QT21.

4.1 Background: RIASes The RIASes are promising combinations of tasks, domains, users, industrial actors, demon-strators, innovation mechanisms, data, etc. They provide a real-world context and framework in which to consider the goals of QT21. By being as concrete as possible the RIASes chosen for QT21 provide a basis to convince decision makers, industry partners, researchers, fund-ing agencies, and others of the goals of QT21 and to demonstrate their value. They specify languages, workflows, organization of R&D, public-private-partnership, platform require-ments, etc. They should also fit into a long-term vision of innovation rather than predicting developments that would happen in the near-term future anyway. The following three RIASes have been selected in the planning process:

1. Corporate: Communication-intensive industry sectors such as automotive or medical that cover a wide range of translation needs from internal documentation to market-ing material;

2. Public: All types of multilingual public communication on the European or national level including NGOs. Types of discourse include emergency warnings, pan-European discussion on topics such as energy policy, eGovernment, or public ser-vices;


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3. Media: Multilingual access to media content by way of subtitling, dubbing, audio de-scription, etc. Domains and tasks include public broadcasting, news/entertainment, lectures, public fora talks, etc.

More details will be provided in sections below. Working groups have been formed to work out four RIAS proposals mentioned above. Members are listed in Table 1 (group leaders are marked in bold face).

Collaborative work spaces have been set up for the groups. Some of the groups have met in virtual meetings to discuss and work on the RIAS; others have communicated via e-mail. The groups are open for input of any Planning Panel member. Note that the clusters and teams are subject to change in the course of the further planning process.

4.2 Description of the RIASes

4.2.1 Corporate The Corporate RIAS deals with innovation in targeted sectors. As examples, automotive and medical have been chosen as both sectors exhibit a number of interesting properties, com-monalities, and divergences as detailed below. In their entirety, both cover many important aspects of the translation needs found in business.

4.2.1.1 Motivation Export-oriented manufacturing industries are a huge factor in Europe‘s competitiveness. Global business and global markets have led to an increased need for high-quality transla-tion both internally and for communication with external business partners and customers. This increased need has led to resource problems on the side of Language Service Provid-

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RIAS Working group members

Cor

pora

te

Medical

Ondřej Bojar Stephen Doherty Serge Gladkoff Declan Groves Jan Hajic Andrejs Vasiljevs Pierre Zweigenbaum

Automotive

Marcello Federico Josef van Genabith Serge Gladkoff Kim Harris Stephan Oepen Volker Steinbiss Hans Uszkoreit

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RIAS Working group members

Public

Núria Bel Stephen Doherty Serge Gladkoff Spyridon Pilos Stelios Piperidis Johann Roturier Lucia Specia Hans Uszkoreit Andrejs Vasiljevs

Media

Aljoscha Burchardt Stephen Doherty Marcello Federico Serge Gladkoff Jan Hajic Stelios Piperidis Lucia Specia Volker Steinbiss

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Table 2: RIAS working groups and members (group leaders listed in bold face).


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ers (LSPs), as it is becoming more and more difficult to find skilled translators, especially for emerging and uncommon language combinations. At the same time, price pressure calls for technologies that increase productivity, first and foremost innovative high quality machine translation (HQMT). The goals of this RIAS are to:

• Drastically increase the bandwidth of the existing localization industry and improve its quality to create more cost-effective workflows that achieve productivity gains and higher throughput;

• Provide a better and more accessible path to the language service profession, and empower language professionals, including translators, vis-à-vis HQMT to create jobs for specialists including (bilingual) post-editors, MT curators (i.e., those who build and maintain MT systems), terminologists, and (monolingual) MT pre-editors;

• Decrease turnaround time and improve consistency, bring knowledge and semantics and the meaning into practical technology, and reuse knowledge better;

• Push for translation of content that has not previously lent itself to MT, thereby ex-panding the currently limited boundaries of MT applications;

• Improve interoperability and decrease technological barriers; • Direct and modernize language technology to align with predicted future trends.

While the globalisation of automotive industries started much earlier, the medical industry is facing similar issues today as an ageing population, migration, and falling borders have in-creased the demand for high-quality translation. Much of the information dealt with in this RIAS is structured based on existing ontologies and terminologies and has a clear semantics (physics, physiology, etc.). One challenge is to in-clude these resources into MT. Other information, such as user discussions in online forums, however, exhibits properties of spontaneous and formally incorrect language, and will thus require robust and fast MT technologies that can adapt to and compensate for these issues and will therefore be addressed in this dedicated RIAS.

4.2.1.2 Tasks and Content Types The following list provides some more specific tasks within the corporate RIAS. Automotive

o Tasks Effective management of large translation memories (TMs), well devel-

oped terminologies, style guides, processes with QA Leveraging TMs with fewer matches, corporate terminologies, less devel-

oped processes Optimising for small (or no existing) TMs, limited terminology compliance,

and underdeveloped/non-existent processes

o Content Types Technical documentation Training material, education, process documentation User manuals, repair and maintenance instructions B2B/Internal communication Internal inbound documents E-mail, expert group communication External customer or owner created inbound documents Customer communication


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Medical o Tasks

Facilitation of professional information delivery and search Search in text resources in multilingual contexts Accessibility to multilingual medical information for public consumption Provision of drug information in multiple languages Facilitation of drug / pharmaceutical information gathering from multi-language

sources Facilitation of clinical trials in multilingual environments

o Content Types

Content for medical professionals (both clinical specialists and GPs) and health authorities

In-hospital databases Medical and health-related information for the general public Treatment and pharmaceutical information Multilingual FAQs, blogs, chats, etc. User-generated questions to medical information websites Emergency situations and service communications Posting of (early) warning messages to the integrated rescue/emergency system

channels, e.g. earthquakes, etc. where multilingual content (e.g., SMS) needs to be processed quickly and accurately

Table 3 presents tasks that the Planning Panel members selected as most promising. It re-lates these tasks to examples of different content types that should be treated.

Task Domain-restricted outbound5

Domain-centred

outbound

Internal inbound User-generated content

Speech

Aut

omot

ive

Technical docu-mentation

Manuals, maintenance documentation

Web-content, marketing mate-rial

Inside triage and overview of outside technical reports, manuals, literature

Fora of customers, brand fans, car lovers, drivers of competition products

Not at this stage

Internal commu-nication

Internal reports, material for expert groups

Intra-corporate communication

Broadcast an-nouncements

Communication of distributed expert groups, intranet fora

Not at this stage

Med

ical

Information for the general pub-lic

Yes Yes Some scenarios Little Not at this stage

Monitoring emergency warnings

Yes In part Some scenarios No Not at this stage

Table 3: Content types in selected tasks

5 “Domain-restricted” here refers to texts that are strictly limited to domain-specific content; “Domain-centred” refers to those that are less strictly tied to a domain.


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4.2.1.3 Users and Industrial/Official Actors The users and industrial actors differ for each scenario. For automotive, these would be lan-guage industry companies and corporations and manufacturers in technical industries as well as the car companies themselves, including Volkswagen, Daimler, PSA Peugeot Citroën, Renault, Fiat, Volvo, Jaguar, Land Rover, etc. Industrial actors are international corporations whose corporate language is not English and LSPs that translate from source languages other than English. Likewise, private SMEs such as German private precision mechanics equipment firms, French producers, etc. and LSPs with experience in post-editing would qualify as would independent language professionals (freelancers). In the medical area, technology development companies are likely potential users who stand to benefit greatly from innovations in this RIAS and medical and healthcare establishments and professionals such as hospitals and specialised clinics that need to process and search reports and other documents. General practitioners may need HQMT for their education, for looking up specific problems, or for treating foreign patients. Additionally, medical device and pharmaceutical companies need HQMT for access to new and emerging markets both within and outside Europe. Official actors include governments and their agencies like the police, emergency/integrated rescue services, disaster response teams, government agencies (min-istries, health-risk assessment offices…) that have need for health-risk communication (e.g., “bird flu”-like cases) or to communicate warnings of large-scale poisonings (e.g., E. coli or methanol contamination…). The same can apply to medical-related NGOs who help in for-eign countries and need to process (for example) disaster responses. Lay users include pa-tients and the general public, who increasingly access general medical information or drug information via online sources.

4.2.1.4 Preliminary Assessment of the RIAS The overall assessment of the RIAS is positive as its research goals are on the one hand flexible, but on the other, the requirements are clear, resources exist, and customers them-selves have a strong interest. The following pros, challenges, and uncertainties provide more detail: Pros

• Appealing domains to stakeholder network, especially for industry involvement • Universally important and attractive to funding • Obvious natural focus on HQMT • Heavily multilingual environments • Representation of truly global industries • Need for expansion beyond EU languages • Great challenges for core research • Scale and speed requirements are very flexible, with few scenarios requiring immedi-

acy • Data and experience already available • Other related medical/LT projects such as Khresmoi6 • Use of resources already available in the field itself: medical ontologies, texts, etc.

6 http://www.khresmoi.eu/


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• Existing volume of data (TMs), terminology, assessment schemes • Well-developed and constrained scenarios (e.g. technical manuals) • Established processes and best practice • Strong client interest (automotive and medical industry) • Task progress indicators are relatively easy to determine • Access to LSPs and technology providers with sufficient experience • Well-defined contexts and sub-contexts to simulate world knowledge thus enabling

high-quality translation services • Ontology-using/-based approaches are valuable to MT improvements • Future prospects in customer/patient treatment and communication

Challenges

• Relatively few existing big players on the service provider side • Not clear whether the big clients will be able to contribute much themselves • Fall-back techniques will need to be proposed explicitly • Need for strict division of public and confidential/internal data • Added overhead, e.g., confidential data for patient in medical contexts • Uncertainties and currently unidentified risks • Multitude of formats and terminologies • Service not trusted by users, i.e. data sharing may not be easy to achieve • New legislation may change the landscape, e.g., personal data, anonymisation, and

sharing. • Output not trusted or verified, therefore, users have not yet adopted these technolo-

gies • Required usability not yet achieved, especially for professionals • Need to expand beyond EU languages where not enough data is currently available

4.2.2 Public Starting from the observation by Umberto Eco that “the language of Europe is translation”, machine translation offers an immediate and effective basis for provision of multilingual ser-vices as part of European public infrastructure. As outlined below, MT would serve many different public communicative purposes by:

• Supporting public services and publicly available services (e.g. e-Government, healthcare) to help public administration to reach multilingual citizenship, and imme-diately deliver urgent information of critical importance;

• Enabling access to public engagement of European and national institutions in all of-ficial European languages;

• Enabling participation of multilingual citizenship in pan-European discussions through online media;

• Enabling access and usage of European multilingual cultural heritage; • Opening access to multilingual eLearning content and solutions.

4.2.2.1 Motivation The EU was created as a multilingual space, but implementing multilingualism is an on-going challenge, e.g.:

“[The] Commission [has] to ensure that every EU citizen's right to address the EU in-stitutions in any of the EU official languages is fully respected and implemented by


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ensuring that public consultations are available in all EU official languages,[...] and that there is no language-based discrimination [...]”

(European Parliament resolution 2012/2676[RSP])

Part of the domain public consultation and participatory communication consists of user-generated content. HQMT technologies thus have to be both robust in dealing with sponta-neous language and combined with text analytics to help access large amounts of data such as that found in long-running online discussions. At the same time, special domains such as law or technical communications come with completely different requirements for precise HQMT. Therefore, scalable and flexible solu-tions that can be easily combined and adjusted are needed to serve this broad multilingual field of public communication.

4.2.2.2 Tasks and Content Types As noted above, the number of tasks and content types that fall under the Public RIAS is vast. In order to implement it, pragmatic decisions will need to be taken, guided by aspects such as the availability of text material or legacy issues. Possible focus areas include:

• Tasks o Facilitation of accessible information delivery in multilingual contexts o Provision of inclusive and high-quality national and European public services o Facilitation of EU-driven requirements for legal and educational services, and

improvements to public safety and security communications • Content Types

o Judicial and legal domains, laws, rules, regulations, etc. o Court proceedings transcripts translation (transcripts now compulsory in EU) o Translation court opinions / decisions / rulings, and regulatory filings o Contracts (public and private) o Colloquial language in social communications o Security alerts (food, epidemics, computer virus, etc.)

• Domains o Public regulation (finance, energy, agriculture, public safety, environmental

protection, etc.) o Educational sector (e-learning) o Cultural sector including content in older and regional variations

All of the above would apply at all levels (ICC, Strasbourg, national level, regional)

Table 4 presents tasks that the Planning Panel members selected as most promising. It re-lates these tasks to examples of different content types that should be treated. Task Domain-

restricted outbound

Domain-centred out-bound

Internal in-bound

User-generated content

Speech

Making content and communica-tion accessible

Public consul-tations of the EP

Emergency warnings

Public hearings Cross-border forum discus-sions (e.g. foreign policy, energy)

Not at this stage

Distributed public services

Regulatory information

Legal infor-mation ser-

Filing b2b re-quests between

Citizens’ re-quests to the

Not at this stage


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services vices public admin-istrations

service provid-er (free text fields)


4.2.2.3 Users and Industrial/Official Actors The key players in this RIAS comprise three layers. The users of the services are in the first place the European citizens. The requesters include national and regional public administra-tions European Union institutions (e.g. EP, EU publication office), cultural institutions, educa-tion institutions, and also the private sector (e.g. lawyers, travel agencies) and NGOs provid-ing advanced services that use public services, e.g., tourism, judicial information. The devel-opers of public services include SMEs in the core MT business, but also language intelli-gence providers in a wider sense that includes services such as transcription (integration of MT), legal information system provision (indexing, CLIR, etc.), and business intelligence (e.g. strategic planning, data extraction, document classification and metadata generation).

4.2.2.4 Preliminary Assessment of the Public RIAS This RIAS has been suggested as it targets societal needs and as the solutions will provide valuable information and tools for MT in general. Moreover, the focus on services will make it easy to measure improvements and show results. A major weakness is the large variety of domains, types of text, and intended uses of translations: it may be difficult to put all these together and have a solution that is good for all cases. The pros and challenges mentioned in the discussions are: Pros

• Results can achieve social impact and contribute to the widespread use of MT tools • Distributed services foster diversification of the MT business and MT readiness • Increasing demand - more and more content is produced, fast translation necessary,

especially for legislation/representatives of stakeholder groups • Broad use of content technologies/(big)data/use cases ("bridging")

Challenges

• Gaps in understanding between society and policy drivers and makers • Volume and diversity of content to be translated • User-generated content is a moving target and development has to anticipate it accu-

rately or effort will be wasted • The MT industry may not diversify itself or accept risks • Quality of translation may not be sufficient • Technological gaps for number of languages • Complexities dealing with many stakeholders - EU, national and other institutions • No dedicated industry behind that support the RIAS, but just opening new avenues

and therefore implying too much risk • Inherent difficulties of handling user generated content

4.2.3 Media This RIAS targets audio-visual translation. More specifically it aims to automate the transfer of essential political and economic news messages across language borders by way of inter- and intra-lingual subtitling, audio description, impact captioning, media search, enhanced


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archives, etc. The long-term goal is to process and cover all media, all channels, all lan-guages, and all domains.

4.2.3.1 Motivation In today’s society, media will play a key role in strengthening the European ideal of “Unity in diversity”. Today, however, there are strong language borders in media consumption. Apart from multilingualism, the demand for accessibility (subtitling/closed captioning) has risen after some countries have started to adopt the European "AVMSD" directive 2010/13/EU, Article 7 ("Member States shall encourage media service providers under their jurisdiction to ensure that their services are gradually made accessible to people with a visual or hearing disability."). Fortunately the same technologies that support accessibility provide ways to improve multilingual support and could be extended to increase accessibility to those who speak different languages. Apart from public and private broadcasting, there are numerous contexts such as archives, newswires, and the web, but also governance information, presentations, and e-learning, where media access is critical already or becoming important sooner than later.

4.2.3.2 Tasks and Content Types This RIAS for the time being includes any type of multimedia such as television, lecture re-cordings, newswire, web recordings, archive material, video games media, etc. However, user-generated content (UGC), as part of other RIASes, is not in the centre of this RIAS. Task Scripted material Unscripted

material Multilingual User-

generated content

Speech

Offline HQ translation (human-aided)

Films, TV series, documentaries

Lectures

Yes — Yes

Online translation TV News (partially scripted)

Newswire Yes — Yes


4.2.3.3 Users and Industrial/Official Actors Stakeholders in this RIAS are on the side of the requester side include content owners, pub-lishers, and developers such as TV broadcasters (e.g., BBC, Telecom Italia, etc.), news/press agencies, media unions, and standardisation bodies (e.g., EBU), and multimedia and video game producers (e.g., Sony, Microsoft). This service provider side includes cap-tioning companies and translation provides in addition to LSPs offering multimedia, AV trans-lation. As this RIAS requires special technology for broadcasting and playback, technical platforms (networks, telecoms), and end-user equipment producers like Sony, Samsung, Apple should be present in the planning activities. The education sector (lectures, slides, recordings) in both traditional education and further education could play a role as well as public bodies generating content. User-generated content will also factor into this RIAS.

4.2.3.4 Preliminary Assessment of the RIAS The appeal of this RIAS is, on the one hand, the societal impact it has and, on the other, the broad range of technologies than can be showcased. The pros and challenges that we iden-tified are: Pros


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• Legislation/representatives of stakeholder groups might demand for more AV offer-ings in the future

• Demand: growing multimedia content makes fast translation necessary • Valuable novelty and innovative quality of research area • Social impact • Cultural differences can be overcome • Visibility, reach • Increased accessibility of web and broadcasting • Broad use of technologies/data/use cases ("bridging")

Challenges

• For broadcasters copyright often seen problematic • Additional requirements to high quality translation (device-related, e.g. TV, tablet,

mobile, etc.) • Can possibly currently/partly be replaced by crowdsourcing • No strong link of LT with image processing and other non-verbal "signs" • Lack of understanding from society and decision makers • Big non-European players (e.g. Google, Asia Online) are taking the lead • Overexposition and expectations • Cultural differences might remain

4.3 Leading-edge Research Plan Starting from the key performance impact factors (KPIFs) defined in the roadmap for the field of MT, this section provides a detailed plan of how to approach research, development, and innovation in the area of HQMT.

4.3.1 From the MT RoadMap to Leading-Edge HQMT In order to break through the translation quality boundaries, two fundamental success criteria need to be taken into account:

a. Translation quality of core MT engines, and b. Productivity of using MT engines in production workflows.

There is dependence between these criteria in that an improvement to (a) will lead to an im-provement to (b)—this is the central motivation to work on (a). At the same time, we are convinced that it is possible to reach improvements for (b) even if improvements to (a) can-not be reached to the extent one would hope or not at the speed that is needed to meet the requirement of industrial cooperation partners. Therefore, we suggest a mixed approach to HQMT research, development, and innovation that balances the risk of putting everything on leading-edge research against the need of European industries for innovations and solutions that can be used productively much soon-er. Figure 1 (overleaf) schematically illustrates the intended temporal sequence of the KPIFs from the RoadMap in relation to a combined measure of translation quality and productivity. Example instantiations of the first three factors in the area of HQMT could be:

• Innovation: Designing a multilingual talking car where all labels and displays are elec-tronic and can adjust to the driver’s language (e.g., for car sharing companies, vehi-cles that allow drivers to ask for operational instructions using a dialog system would have an obvious advantage);


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• New technologies for proven MT approaches: Starting from results of projects such

as MATECAT or EU-Bridge, design a cockpit for improving live subtitling by including quality estimation, user-feedback, and crowd-sourcing into an improved workflow and editing environment;

• Radically new approaches: Take a cognitive approach where the source text is trans-formed into a neural network representation pattern from which the target text is then generated.

The availability of data for training MT engines can be regarded somewhat separately. It will certainly improve over time by natural growth. At the same time, one of the goals of leading-edge HQMT research is to improve methods for using comparable corpora, crawling, on-the-fly generation of corpora for active learning or re-training, quality estimation, etc. The temporal sequence indicated in Figure 1 illustrates that if work on the three KPIF starts right at the beginning of QT21, we can expect quality jumps when the new technologies for proven MT approaches are ready and when the radically new approaches have reached some maturity. But at the same time, the innovative new applications can be used produc-tively by the industry partners practically soon after the start of the project. It can further be expected, that combinations of the above of, e.g., more data with innovative applications will again lead to a boost in MT usability.

4.3.2 The Interplay between Translation Quality and Productivity The approach we suggest is based on the goal to balance the high risk related to the re-search for disruptive new approaches with more predictable iterative improvements through innovation and new technologies for proven approaches. Figure 2 (overleaf) provides a more detailed view on the interplay of the two criteria translation quality and MT productivity over time for the KPIF:

• Radically new approaches will lead to a jump in quality (and thus a little later in productivity) after a period of baseline quality (or even lower for a while);

Figure 1: Schematic timeline for HQMT R&D and innovation


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• New technologies for proven MT approaches will at best lead to a small improvement in quality (where the plateau has not been reached yet), but the productivity will rise

constantly; • Innovation will also lead to increased productivity while the translation quality of the

core engines remains the same. Many concrete strategies and technologies have been suggested by the planning panel that can be used to implement the different types of MT engines, workflows, and applications. Here is a first list of technologies to be used for approaching the challenges defined by the RIAS: MT research strategies

• Many MT systems adapted to different sub-domains and applications • Include information aggregation, paraphrasing, text simplification, captioning, etc. • Document-level processing • Lexical translation quality • Use of context in MT (including non-textual content) • Controlled language / templates • Using quality estimation • Use CAT Tool for on-going projects • User-centric MT, translator is fully integrated into the MT usage and training loop • Crowdsourcing

Strategies regarding languages

• Novel strategies for under-resourced languages and domains • Translation between non-English languages

Figure 2: Estimated development of MT quality and productivity in the first three key performance impact factors


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• Focus on one target language: English as pivot language • Translation of old and regional language variations • Sign languages

Strategies regarding resources

• Automatic production of resources for dynamic needs • Innovative terminology adaptation / lexical quality • Data fusion/transfer (e.g. from transcriptions to captions)

LT research strategies

• Monolingual technologies and resources, probabilistic transfer • Knowledge accumulation and reuse • Semantic data - into the picture, for HQMT • Ontologies • Unlabelled data • Integration of text and speech • Integrate other tools for text consistency and cohesion • Adapt content to particular types of audience (e.g. simplification) • Cross-lingual social divergence/ convergence detection • Quality speech translation

General Research/IT strategies

• Living labs • EU R&D Platform/engine • Trustworthiness (service, source, translation) • Evaluation technology improvement • Distributed services • Open high-performance computing infrastructure • Open standards, open-source tools, and interoperability • Adding metadata • In-Browser work

The inclusion of results of additional analysis, such as syntactic or semantic structure, into the statistical translation process and of statistical selection mechanisms into the rule-based setup are just the first attempts to a tighter integration of paradigms. Any continuation of the search for the best combination and interaction of different mecha-nisms without the proposed analytical mode of research grounded in the collective systemat-ic investigation of quality barriers would amount to a trial-and-error strategy reminiscent of alchemist precursors of contemporary chemistry. Nevertheless, any research action of the intended scope needs to secure a clear benefit to society even if the targeted major scientific breakthroughs cannot be accomplished. Today nobody can predict with certainty how many more years of natural language processing re-search might be needed before theoretically general and practically adequate computational models of the translation process can be developed that combine generic models of lan-guage processing and mapping with the required task-specific expertise and performance. Despite our optimism, which is deeply rooted in experience and scientific conviction, we cannot guarantee that current paradigms of digital computing (including today’s program-


23

ming frameworks and concepts and machine learning techniques) will allow for such power-ful general models in the near future. Therefore the scientifically ambitious thrust for new approaches and paradigms carries a high risk. However, the selected novel approach of MT research opens the way for a parallel strategy of technology evolution that, while academically less attractive, is potentially much more reli-able in outcome. For each observed and measured quality barrier that cannot be surmount-ed by new general models of translation, dedicated solutions will be designed that achieve or at least approximate the desired performance for the affected source-language input seg-ments. To this end, each specialised solution must include a model for the detection of the segments it accommodates. This strategy is somewhat analogous to a successful approach in grammar checking, where one cannot trust generic parsing for the reliable detection and correction of certain error classes but instead has developed dedicated error models with appropriate diagnostics for these cases. In the worst case such a parallel approach might result in bulky systems with large batteries of specialised modules that lack uniformity and elegance. However, using computing re-sources available today, such systems could serve as powerful application services. At the same time they would constitute a baseline system for challenges within and beyond QT21 to improve the technology in elegance, generality, and efficiency.

4.4 Project Structure of QT21 The QT21 MasterPlan is embedded in the MT RoadMap (Section 3) and the Leading-Edge Research Plan (Section 4.3), articulating the first phase (2014-2017) of the MT RoadMap within the Leading-Edge Research Plan for HQMT. The QT21 MasterPlan is based on a novel project architecture supporting a concerted action involving a cluster of satellite pro-jects coordinated by a core project, either instantiated as one project or as a project cluster. The core coordinating project will be responsible for:

• Driving the Key Performance Impact Factors (KPIFs) for the project; • Implementing an element of internal project competition between KPIFs 1 and 2 (Dis-

ruptive New Approaches and New Technologies for Proven MT Approaches); • Orchestrating the articulation of HQMT in three selected RIASes (Research Innova-

tion and Application Scenarios) as established by the QTLaunchPad Planning Panels (see Deliverable D6.2.1) in three satellite projects;

• Ensuring overall project targets and capture of synergies. The three satellite projects will be responsible for:

• Implementing and tuning the core project KPIFs to the requirements of the RIASs de-scribed above;

• Providing data to the core project; • Transferring innovations from the core project into production.


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4.4.1 The QT21 Coordinating Project CP: A Coopetition-Based Approach Progress in the four KPIFs are key to HQMT. The KPIFs cut across (are equally relevant to) each of the RIASs. At the same time they need some level of adjustment to the particulars of the respective RIAS.7 Because of this, an optimal division of labour factors core parts of the KPIFs into the coordinating project. Furthermore, as detailed in Section 3.1, KPIFs 1 and 2 (Disruptive New Approaches and New Technologies for Proven MT Approaches) can achieve performance and productivity gains independently of each other or in combination:

1. In particular, successful development of disruptive new models will lead to substantial translation quality improvements, and hence productivity improvements;

2. On the other hand, new technologies for trusted approaches, may achieve productivi-ty improvements (even without corresponding translation quality improvements in the existing trusted base MT technologies);

3. Finally, new technologies combined with disruptive new models hold the potential of productivity improvements over (1) and (2).

Because of this, progress in Disruptive New Models will effectively compete with progress on New Technologies for Trusted MT Approaches, and will ultimately only be adopted if the gains obtained by Disruptive New Models outperform those gained by New Technologies for Trusted MT Approaches. In addition, it is rather likely that scientists will search for disruptive new models in more than one research direction. Converging too early may nip promising directions in the bud. This introduces an element of competition into the core co-ordinating QT21 project. This competitive facet of the planned endeavour will coexist and hopefully also positively interact with its built-in cooperative facet. The latter follows from the sharing of goals, re-sources, success criteria and evaluation activities and from the planned collective, tightly integrated prototype development tasks.

4.4.2 The QT21 Project Architecture Two possible architectures for QT21 are presented in Figure 3 (overleaf). One illustrates a “cluster” of related projects and the other a more tightly integrated single project with multiple subcomponents.

4.4.3 Estimated Resources 15M euro over 3 years: 7.5–9.0 M for CP, 2–2.5M for each RIAS (Corporate, Public and Me-dia).

4.4.4 Steps Toward Realization An essential part of the MasterPlan is the following outline of the necessary next steps to-ward the realization of the QT21 effort. 7 This is particularly clear in the case of the Media RIAS with its focus on speech-to-speech translation.


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1. Between now and the end of September, QTLaunchPad consortium members, sub-contractors and and external contributors and partners such as the members of the

Planning Panel will continue to promote the plan, the need for action and the ex-pected benefits for European society, business and research at several conferences (MT Summit in Nice, META-FORUM in Berlin, the Unity in Diversity Conference in Vilnius, the Seimas Parliamentary Event in Vilnius, and the EFNIL conference in Vil-nius).

2. Between now and the end of October, the ties with prospective user partners for the

three RIAS will be strengthened. Potential industrial technology provider partners will be screened.

3. An excerpt of the MasterPlan will be distributed to national and regional funding

agencies in member states and associated countries. Letters of Interest, Letters of Intent, MoUs and other signals of support and expressions of intention to fund satel-lite projects will be collected.

4. The further planning of the RIAS, the planning of the shared task and the testing of

the QTLaunchPad quality assessment metric will be used as means to increase co-hesion within the community of potential contributors to QT21.

5. Depending on the technical/scientific contents of the First Call of HORIZON 2020 and

the announced instruments foreseen for quality translation technology research, the QTLaunchPad consortium and the entire planning community will enter into concrete proposal planning in November after the publication of the Call. Main goal will be to determine the best fit between the RoadMap/MasterPlan of QTLaunchPad and the goals, instruments and conditions of the HORIZON 2020 Workplan and First CFP.

Figure 3: Alternative Setups for QT21 – a project cluster (left) or one project (right)


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6. In November and December, the essential elements and partnerships for one or mul-tiple proposals will be finalized in several sub-groups, depending on the contents and conditions of the CFP.

7. In parallel, established contacts to national and regional funding bodies that had ex-

pressed interest in contributions to the QT21 endeavour will be utilized for a focused attempt to prepare a few satellite projects.

8. By the CFP I submission deadline (probably in January or February of 2014) the pro-

posal(s) will be submitted. In parallel, work on satellite proposals will continue and proposal submitted at appropriate times.

9. In parallel with proposal preparation, the QTLaunchPad research infrastructure will

be tested in a shared task and in individual research tasks. Infrastructure and com-pleted resources such as the evaluation metric, the error corpora and the test suites will be promoted and distributed. External resources will be curated and integrated in-to the research infrastructure. The findings of the barrier analysis will be disseminat-ed and promoted.

10. If steps 1.- 9. are successful, and if the proposal(s) are selected for funding, QT21

could have a great start in late Summer or Fall of 2014.

4.5 Communications Plan An inclusive and far-reaching approach to community mobilization has been adopted in light of the need to involve a diverse and dynamic group of stakeholders, namely: industry repre-sentatives (LSPs, SMEs, corporations, translation/localisation buyers and vendors, R&D groups, etc.), funding bodies, research and academic institutions, and communities of trans-lation and language technology users. To this end, we are using a growing online network that consists of: the project homepage and mailing list; several popular social media outlets (LinkedIn, Twitter, Facebook); existing networks of the project partners (e.g. DFKI, CNGL, DCU, USFD, ILSP), including regular presence to industry communities via GALA webinars, newsletters, as well as other widely circulated translation/localisation outlets (e.g. Multilingual, Proz.com, Siliconrepublic, Com-mon Sense Advisory, Localisation Focus). Web analytics have been implanted to ensure these ventures have been impactful in terms of resource expenditure for desired results. In addition to ramping up an online presence, face-to-face stakeholder engagement has also been a priority, as evident in the project’s presence at several key high-impact events and conferences throughout 2013, including: Tralogy, the GALA conference, MultilingualWeb, Localization World, Optimale, SemEval, ACL, WMT, the MT Summit (September 2013), META-Forum (September 2013), media4all (September 2013), tekom/tcworld (November 2013), and GALA’s subsequent conference (March 2014). As follow-up to establishing further community mobilization within QT21, one-to-one interac-tions with potential industry collaborators have been made possible thanks to our on-going communication and engagement activities. We currently have strong collaborations with: Volkswagen, SAP, Welocalize, Centre for Next Generation Localisation industry partners, other EC-funded projects, including META-NET, individual translators, user groups such as


27

GALA CRISP and OPTIMALE, and professional bodies such as FIT. Informed by this, the overall goal for the QT21 Communication Plan is one that is:

a. efficient; b. centralised and aligned with project goals; c. flexible and open communication.

In order for this to be realised, the following communication cycle will be implemented - see Figure 4. This lean cycle requires an efficient and usable communications platform that can be tailored to the needs of the QT21 project structure. We will provide opportunities for ex-ternal input to be given in the form of open and on-going public consultation periods, and invitations for input to targeted experts and user groups. A key feature of the cycle is its de-fined structure, which aims to achieve specified goals, while at the same time being suffi-ciently flexible to meet the needs of developing project themes. The cycle is seen as aggre-gator of themes and media so as to take input from people in various media and integrate it to the appropriate line of discussion. The cycle will also include an inclusive mechanism for innovation where industry-facing ac-tivities will go hand-in-hand with QT21 development, thus fostering opportunities for partner-ship, commercialisation, and sustainability and diversity of funding sources. An innovation pipeline is the channel through which this will be implemented throughout the project lifecy-cle. This approach addresses gaps between initial ideas and their actual realisation via in-clusive and on-going interactions between researchers, developers, and industry partners. A key element is the inclusion of potential technology transfer from the beginning of the initia-tive rather than waiting until project maturity to exploit potential ideas. QT21 will therefore be accessible to external industries beyond existing stakeholder group, e.g. venture capitalists. Throughout the project lifecycle several activities will support and develop innovation, name-ly:

Figure 4: Lean Communications Cycle for QT21


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• Implementation of a charrette model for inclusive consultation and collaboration (e.g. as successfully used for previously commercialised research projects, such as CNGL’s MT Watch and TMT Prime);

• Face-to-face innovation bootcamps within RIASes; • On-going series of short research pitches paired with outreach webinars supported

by GALA; • Strategic matchmaking of researchers with developers and industry contact points.

In parallel, training is also a focus within the communications cycle. Training events will be scheduled at regular intervals to support industry-focused skills-building, which will be attrac-tive to a range of organisations and will relate to project aims and outcomes. Here, the de-velopment and maintenance of freely available and easily accessible training materials is a central component to a successful and far-reaching training programme. Knowledge transfer in the form of expert training represents a unique selling point for industry as new skills and expertise can easily be translated into productivity and quality gains, e.g. training in cutting-edge translation technologies and tools, and localisation techniques. Targeted domains in-formed by the RIASes embody especially lucrative points of industry contact given the value of unique skillsets, e.g. post-editing MT, and audiovisual translation. In addition to external participants, training enables the sharing of skills and know-how across the project consorti-um, a key factor for successful and rewarding in-reach activity. Face-to-face events targeted at high-impact conferences and industry events will continue to increase project visibility and community outreach, when paired with the online events de-scribed above, a much more substantial reach is achieved. Such an inclusive approach to training is also a way to garner participation for further and new interactions throughout QT21 as well as specific events such as the shared tasks and specialist workshops. Lastly, as the backbone of a successful research initiative, both high-impact scientific and industry publications will be targeted and tracked in a consistent manner. Key academic and industry conferences represent potential for easily attainable visibility and access to dissem-ination. By continuing to develop QTLaunchPad’s growing network of industry stakeholders, e.g. GALA, further access to industry outlets strengthens mechanisms for communications within QT21.

4.6 Resource Acquisition Plan QTLaunchPad will simplify access to the necessary language resources (datasets and tools) and of all those support mechanisms for the identification, acquisition, documentation and sharing of MT-related data sets and language processing tools. Language resources en-compass monolingual and multilingual data sets, structured (i.e. lexica, terminological data-bases, thesauri) and unstructured (i.e. raw text corpora), as well as language processing tools such as parts-of-speech taggers, parsers, named entity recognisers, etc. QTLaunchPad builds upon and extends the META-SHARE infrastructure (www.meta-share.eu, www.meta-share.org) to cater for the resources dimension and provide the support mechanisms needed for documentation, sharing, search, and retrieval of all MT-related re-sources. To this end, a QTLaunchPad-dedicated META-SHARE node/repository will be made availa-ble. The RIASes specify the targeted language data for MT system training, development, and evaluation as determined by the Planning Panel. The sources of the targeted data and tools will consist of:


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• Resources already available in the META-SHARE network, preferably under permis-

sive terms, allowing at least research use; • Data sets coming from the QTLaunchPad consortium partners and the user base that

QTLaunchPad is mobilizing. Such data sets reside with language service providers or owners of the original content (e.g. manuals in the automotive sector available in mul-tiple languages) and we expect that QTLaunchPad will be able to motivate the own-ers to open up earlier versions of their content for research use;

• Domain and genre-specific data to be discovered automatically (mainly from the web) in order to fill in missing data sets. This type of data will be discovered through fo-cused web-crawling tools, both for the monolingual and the multilingual dimensions of the required data.

Table 6 (overleaf) presents a summary of indicative datasets per RIAS, as a result of the first Planning Panel meetings. D4.1.1 will present datasets already available in META-SHARE, as well as datasets now being documented and soon available through the QTLaunchPad–dedicated META-SHARE node/repository. Additional datasets that will feed in all RIASes, especially the corporate (domain-focused) RIAS, will be discovered automatically through efficient and distributed focused crawling tools in order to collect web documents in specific domain and genres. The focused crawling tools aim to discovering both monolingual and multilingual data for the specific domains and genres, and they will be made available as open-source tools. The tools will make use of legal or permission-of-use information of the crawled data, where such information exists, and all data available under a sufficiently permissive licence will be made available to the QT21 action.


RIAS Resources (focus on language data) Existing Being created

Cor

pora

te

Med

ical

World Health Organisation (WHO) materials Websites with FAQs, replying

to general public questions Lexical and healthcare-internal resources, e.g. MeSH, ICD, RADLEX, etc. (Structured datasets: thesauri, ontologies, etc.)

Internal materials (hospitals, pharmaceutical industry)

Scientific material (articles in open access journals) Health/lifestyle (e-) magazines Specific (EMEA, European Medicines Agency), gen-eral parallel texts for many languages

Arbitrarily chosen monolingual example: http://www.bbc.co.uk/health/

TDA/TAUS parallel data ECDC-TM (Professional translation: English language web pages of the European Centre for Disease Pre-vention and Control, EU agency, Stockholm)

Aut

omot

ive

User manuals, repair and maintenance instructions Customer or owner created inbound documents Large TMs, well developed terminologies, style

guides, processes with QA B2B/Internal communication Training material, process documentation TMs with fewer matches, corporate terminologies, less developed processes Internal inbound documents Email, expert group communication

Publ

ic

EU and National legislation, local regulations (incl. public procurement notices)

User generated content: Blogosphere, Social networks, Cross media user messages, e.g. SMS, e-mail, etc. (user generated content may be of low quality)

Judicial / Legal (all available publicly) Government and related communication (announce-ments, feedback, PR, etc., security alerts) Cultural domain data (Europeana, Digital libraries) Educational domain data

Med

ia

Large volumes of lecture data (e.g. TED in English, with human translations in 90 language)

Continuous flow of lecture data

Talks in other countries without translations Continuous flow and provision of broadcast material Movies and TV series subtitled by Open subtitles (vol-

unteers, fans) Movies subtitled and translated by “Translators with-out Borders” Mono- and multilingual broadcast material, e.g. Eu-ronews, BBC (IPR issues involved) European Parliament Plenary Sessions (EPPS) - in-cludes regional dialects and non-native speech, ac-companied by verbatim and normalized transcripts.

Table 6: Indicative datasets per RIAS, as a result of the first Planning Panel meetings


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5 Appendix: List of Planning Panel Members Name Ondřej Bojar Núria Bel Aljoscha Burchardt Stephen Doherty Marcello Federico Hans Fenstermacher Serge Gladkoff Jan Hajic Kim Harris Philipp Koehn Arle Lommel Joseph Mariani Hermann Ney Stephan Oepen Spyridon Pilos Stelios Piperidis Johann Roturier Lucia Specia Volker Steinbiss Hans Uszkoreit Josef van Genabith Andrejs Vasiljevs Alex Waibel Francois Yvon Pierre Zweigenbaum


6 Appendix: META-NET RoadMap “Translingual Cloud” (Version: March 2013)

Research Priority Phase 1: 2013-2014 Phase 2: 2015-2017 Phase 3: 2018-2020 Immediate afforda-ble translation in any needed quality level (from sufficient to high)

Development of necessary monolingual lan-guage tools (analysis, generation) driven by MT needs; exploitation of novel ML tech-niques for MT purposes, using large LR and semantic resources, including Linked Open Data and other naturally occurring semantic and knowledge resources (re-purposing for MT and NLP use); experiment with novel metrics, automated, human-centred, or hy-brid; use EU languages, identify remaining gaps (LR resources, tools)

Concentrate on HQMT systems using re-sults of Phase 1; deepen development of MT-related monolingual tools; employ nov-el techniques aimed at HQMT, combina-tion of systems, domain adaptation, cross-language adaptation; develop show- cases for novel translation workflow; use novel metrics identified as correlated with the aims of HQMT; continue development on EU languages, identify needs for non-EU languages (MT-related) and their gaps

Deployment of MT systems in particular applications requiring HQMT, such as technology export, government and pub-lic information systems, private services, medical applications etc., using novel translation workflows where appropriate; application- and user-based evaluation driven engagement of core and supple-mental technologies; coverage of EU languages and other languages im-portant for EU business and policy

Delivering multi-media content in any language (caption-ing, subtitling, dubbing )

Multi-media system prototypes, combining language, speech, image and video analysis; employing novel techniques (machine learn-ing, cross-fertilisation of features across me-dia types); targeted evaluation metrics for system quality assessment related to MT; aimed at EU languages with sufficient re-sources; data collection effort to support mul-ti-media analysis

Prototype applications in selected do-mains, such as public service (parliament recordings, sports events, legal proceed-ings) and other applications (TV archives or movie delivery, online services at con-tent providers); continued effort at multi-media analysis, adding languages as re-sources become available

Deployment of large-scale applications for multi-media content delivery, public and/or private, in selected domains; de-velopment of online services for caption-ing, subtitling, dubbing, including on-demand translation); new languages for outside-of-the-EU delivery, continued improvement of EU languages

Cross-lingual knowledge man-agement and linked open data

Publication of multilingual language re-sources as linked open data as well as link-ing of resources across languages; develop ontology translation components that can localise ontologies and linked datasets to different languages

Develop an ecosystem of NLP tools and services that leverage the existing multilin-gual resources on linked open data; devel-op new generation of MT technology that can profit from semantic data and linked open data

Develop methods that allow querying linked open data in different languages

Avant-garde func-tionalities

Consecutive interpretation and translation Synchronous interpretation and translation Translingual collaborative spaces

workshop “mt roadmap and masterplan” and results · d6.8.1 workshop “mt roadmap and...

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