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Automated Writing Assistance: Grammar Checking and Beyond Topic 5: Beyond the Sentence
Robert Dale Centre for Language Technology
Macquarie University
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Outline
• Frontiers: Where We Might Want to Go
• The View from Natural Language Generation
• Closing Remarks
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Frontiers
• Writing assistance …
– Beyond the sentence
– Beyond syntax
– Beyond revision
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Flower and Hayes’ Cognitive Process Model
Flower and Hayes 1981
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The Nature of the Revision Process
Faigley and Witte 1981
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Outline
• Frontiers: Where We Might Want to Go
• The View from Natural Language Generation
• Closing Remarks
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What is NLG?
• Goal:
– computer software which produces understandable texts in English or other human languages
• Input:
– some underlying non-linguistic representation of information
• Output:
– documents, reports, explanations, help messages, and other kinds of texts
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‘Meaning’
Text
Natural Language
Understanding
Text
Natural Language
Generation
NLP = NLU + NLG
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Inputs and Outputs
The inputs to NLG:
• A knowledge source
• A communicative goal
• A user model
• A discourse model
The output of NLG:
• A text, possibly embodied as part of a document or within a speech stream
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Component Tasks in NLG
1 Content determination
2 Discourse planning
3 Sentence aggregation
4 Lexicalisation
5 Referring expression generation
6 Syntactic and morphological realization
7 Orthographic realization
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1 Content Determination
• The process of deciding what to say
• Can be viewed as the construction of a set of MESSAGES from the underlying data source
• Messages are aggregations of data that are appropriate for linguistic expression: each may correspond to the meaning of a word or a phrase
• Messages are based on domain entities, concepts, and relations
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2 Discourse Planning
• A text is not just a random collection of sentences
• Texts have an underlying structure in which the parts are related together
• Two related issues:
– conceptual grouping
– rhetorical relationships
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3 Sentence Aggregation
• A one-to-one mapping from messages to sentences results in disfluent text
• Messages need to be combined to produce larger and more complex sentences
• The result is a sentence specification or SENTENCE PLAN
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4 Lexicalisation
• So far we have determined text content and the structuring of the information into paragraphs and sentences, but the raw material is still assumed to be in the form of a conceptual representation
• Lexicalisation determines the particular words to be used to express domain concepts and relations
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5 Referring Expression Generation
• Referring expression generation is concerned with how we describe domain entities in such a way that the hearer will know what we are talking about
• Do we use a proper name? A definite or indefinite description? A pronoun?
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6 Syntactic and Morphological Realization
• Every natural language has grammatical rules that govern how words and sentences are constructed
– Morphology: rules of word formation
– Syntax: rules of sentence formation
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7 Orthographic Realization
• Orthographic realization is concerned with matters like casing and punctuation
• This also extends into typographic issues: font size, column width …
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Tasks and Architecture in NLG
• Content determination
• Discourse planning
• Sentence aggregation
• Lexicalisation
• Referring expression generation
• Syntax + morphology
• Orthographic realization
Document
Planning
Micro Planning
Linguistic
Realization
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A Pipelined Architecture
Document
Planning
Microplanning
Surface
Realisation
Document Plan
Text Specification
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Microplanning Help
• Paraphrase
• Sentence simplification via summarisation techniques
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Aggregation
Combinations can be on the basis of
• information content
• possible forms of realisation
Some possibilities:
• Simple conjunction
• Ellipsis
• Embedding
• Set introduction
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Some Examples
Without aggregation:
– Heavy rain fell on the 27th. Heavy rain fell on the 28th.
With aggregation via simple conjunction:
– Heavy rain fell on the 27th and heavy rain fell on the 28th.
With aggregation via ellipsis:
– Heavy rain fell on the 27th and [] on the 28th.
With aggregation via set introduction:
– Heavy rain fell on [the 27th and 28th].
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An Example: Embedding
Without aggregation:
– March had a rainfall of 120mm. It was the wettest month.
With aggregation:
– March, which was the wettest month, had a rainfall of 120mm.
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Rhetorical Structure Theory
• Basic idea:
– The elements of a text are connected together by rhetorical relations
– A text is coherent by virtue of the presence of these relations---if the text cannot be analysed in these terms then it is not coherent.
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Text Structure
You should come to the Northern Beaches Ballet performance on Saturday. I’m in three pieces. The show is really good. It got a rave review in the Manly Daily. You can get the tickets from the shop next door.
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Beyond Pairs of Sentences
S1: You should come to the Northern Beaches Ballet performance on Saturday.
S2: I’m in three pieces.
S3: The show is really good.
S4: It got a rave review in the Manly Daily.
S5: You can get the tickets from the shop next door.
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The Ballet Text
You should ... I’m in ... You can get ... The show ... It got a ...
MOTIVATION
MOTIVATION
EVIDENCE
ENABLEMENT
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An RST Relation Definition
Relation name: Motivation
Constraints on N:
Presents an action (unrealised) in which the hearer is the actor
Constraints on S:
Comprehending S increases the hearer’s desire to perform the action presented in N
The effect:
The hearer’s desire to perform the action presented in N is increased
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Outline
• Frontiers: Where We Might Want to Go
• The View from Natural Language Generation
• Closing Remarks
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Conclusions
• Current technology only scratches the surface in terms of the kinds of support we would like to give to authors
• Almost any aspect of NLP technology can be pressed into service to support authors
• NLG techniques provide a rich source of ideas for how to build symbiotic systems that take advantage of the knowledge and capabilities of both human and machine
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Who Today’s Main Players Are
• Microsoft
• Educational Testing Service
• Activities around the University of Cambridge
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Finding Out More
• ACL Workshops on Innovative Use of NLP for Building Educational Applications: 2011 was the sixth in the series
• Relevant material often found in journals outside the normal ‘ACL space’:
CALICO Journal College Composition and Communication Computers and Composition Computer Assisted Language Learning, Journal of Second Language Writing
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