1 commercial applications of natural language technology april 14, 2005 deborah a. dahl principal,...

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Commercial Applications of Commercial Applications of Natural Language Natural Language

TechnologyTechnologyApril 14, 2005April 14, 2005

Deborah A. DahlDeborah A. DahlPrincipal, Conversational TechnologiesPrincipal, Conversational Technologies

Chair, World Wide Web Consortium Multimodal Chair, World Wide Web Consortium Multimodal Interaction Working GroupInteraction Working Group

[email protected]@conversational-technologies.com

22Conversational Technologies

Business MotivationsBusiness Motivations

save money (operators, phone costs)save money (operators, phone costs) improve user satisfactionimprove user satisfaction provide new revenue-generating provide new revenue-generating

servicesservices do something that couldn’t otherwise be do something that couldn’t otherwise be

donedone legal requirementslegal requirements


Technical Motivations for Technical Motivations for Natural Language ProcessingNatural Language Processing

indirection

complexity of intention

complexity of language

I’ve been having a lot of problems with my inkjet printer the last few weeks

I’ve been having a lot of problems with my inkjet printer the last few weeks

system: When do you want to depart?user: I need to be downtown for an 8:00 meeting

system: When do you want to depart?user: I need to be downtown for an 8:00 meeting

•travel from San Francisco to Philadelphia•aisle seat•vegetarian meal•no more than one stop•red-eye ok if arrives after 6:00 a.m. and doesn’t stop in Chicago•wheelchair needed•should be on one of my preferred airlines unless fare is much higher

•travel from San Francisco to Philadelphia•aisle seat•vegetarian meal•no more than one stop•red-eye ok if arrives after 6:00 a.m. and doesn’t stop in Chicago•wheelchair needed•should be on one of my preferred airlines unless fare is much higher

system: Where do you want to go?user: Philadelphia

system: Where do you want to go?user: Philadelphia


Natural Language Understanding in a Natural Language Understanding in a Spoken Dialog SystemSpoken Dialog System

RecognizerMeaning

extraction

Dialog manager

Back-end application

Generate prompt

Speech generation

Language model

Acoustic modelsNLU rules

DM rules

NLG rules

Templates

TTS

Recordings


Natural Language Processing in Natural Language Processing in Commercial Spoken Dialog Commercial Spoken Dialog

SystemsSystems Form-filling applicationsForm-filling applications Classification of free-form spoken inputsClassification of free-form spoken inputs StandardsStandards


Form-filling Spoken Dialog SystemsForm-filling Spoken Dialog Systems

retail bankingretail banking voice portalsvoice portals access to email, voice mailaccess to email, voice mail travel reservations (Amtrak Julie)travel reservations (Amtrak Julie) package trackingpackage tracking


Multimodal Form-Filling using Multimodal Form-Filling using XHTML+VoiceXHTML+Voice

IBM Chinese food demoIBM Chinese food demo


Government Applications -- NASAGovernment Applications -- NASA

Clarissa -- International Space Station's new Clarissa -- International Space Station's new speech-powered virtual assistantspeech-powered virtual assistant

Space station checklists are very long and Space station checklists are very long and complex with many branches, which often complex with many branches, which often require 'fill-in-the-blank' answers. require 'fill-in-the-blank' answers.

General purpose 'procedure reader’General purpose 'procedure reader’ helps astronauts check out space suits and helps astronauts check out space suits and

analyze drinking water quality analyze drinking water quality Scheduled to begin working with astronauts in Scheduled to begin working with astronauts in

May as part of International Space Station May as part of International Space Station Expedition 11. Expedition 11.


Statistical ClassificationStatistical Classification

Sort user’s statement into bins of predefined Sort user’s statement into bins of predefined topics (for example, place order, find out topics (for example, place order, find out status of order, return item)status of order, return item) given examples of statements that go in given examples of statements that go in

different bins (training data)different bins (training data) sort new examples into the right binssort new examples into the right bins

example of applying these kinds of example of applying these kinds of techniques to text – spam filterstechniques to text – spam filters


Example of Classification: Spam Example of Classification: Spam FiltersFilters

FOR YOUR ATTENTION; Dear Sir, I am FOR YOUR ATTENTION; Dear Sir, I am pleased to write you in view of the pleased to write you in view of the circumstances in which I now found circumstances in which I now found myself. This rescuable situation, though myself. This rescuable situation, though with its attendant mutual benefit needs with its attendant mutual benefit needs urgent action hence this letter,and I do urgent action hence this letter,and I do hope you will not hesitate to come to my hope you will not hesitate to come to my rescue….rescue….

90%

84%

93%

98%84%


Some Current Statistical Some Current Statistical Classification SystemsClassification Systems

Nuance “Say Anything”Nuance “Say Anything” Scansoft “SpeakFreely”Scansoft “SpeakFreely” ATT “VoiceTone”ATT “VoiceTone” BBN “Call Director”BBN “Call Director” TuVoxTuVox


Customer Service CallsCustomer Service Calls

Touchtone menu is complex Touchtone menu is complex with many layerswith many layers

Prompts are confusingPrompts are confusing

Customer wants just to say Customer wants just to say what they need.what they need.

“I’m closing up my summer home and want to turn off the phone.”Problem:

Customer Service Destination?

Entry Point:

Billing Payment

Makearrangements

RepairCancel Service

Orders

Copy of Bill

Unauthorized call

Balance Past due notice

New Service

OrderStatus

Seasonal

OrderMove

OrderChange

Pay now

Conversational Technologies

BBN Call DirectorBBN Call Director™™

AutomatedServices

Sales

Billing

TechnicalSupport

Speech

Text

IVR RouterTopic

“Please tell me briefly the reason for your call today.”

Speech Recognizer

Topic Classifier

Statistical Grammars & Topic Models

“I’m calling to check whether there is any better rate plans

than the one I currently have.”


StandardsStandards

Extremely important for commercial Extremely important for commercial applicationsapplications


W3C Natural Language StandardsW3C Natural Language Standards

Aimed at form-filling dialogsAimed at form-filling dialogs VoiceXML – defines dialogsVoiceXML – defines dialogs Speech Recognition Grammar Specification Speech Recognition Grammar Specification

(SRGS): describes allowable sequences of (SRGS): describes allowable sequences of wordswords

Semantic Interpretation (SI): describes how Semantic Interpretation (SI): describes how sequences of words are to be interpretedsequences of words are to be interpreted

Extensible MultiModal Annotation (EMMA) Extensible MultiModal Annotation (EMMA) represents final interpretation of user’s inputrepresents final interpretation of user’s input


Form-filling DialogForm-filling Dialog

System: Welcome to the weather information service. What state?

User: help

System: Please speak the state for which you want the weather

User: Pennsylvania

System: Please speak the city for which you want the weather.

User: Philadelphia


VoiceXML ExampleVoiceXML Example<?xml version="1.0" encoding="UTF-8"?> <vxml version="2.0" xmlns="http://www.w3.org/2001/vxml"

xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/vxml http://www.w3.org/TR/voicexml20/vxml.xsd"> <form id="weather_info">

<block>Welcome to the weather information service.</block> <field name="state">

<prompt>What state?</prompt> <grammar src="state.grxml" type="application/srgs+xml"/> <catch event="help"> Please speak the state for which you want the weather. </catch>

</field> <field name="city">

<prompt>What city?</prompt> <grammar src="city.grxml" type="application/srgs+xml"/> <catch event="help"> Please speak the city for which you want the weather.</catch>

</field> <block>

<submit next="/servlet/weather" namelist="city state"/> </block> </form>

</vxml>


SRGS ExamplesSRGS Examples

Context-free grammarContext-free grammar XML and ABNF formats are providedXML and ABNF formats are provided

<rule id="yes"> <one-of>

<item>yes</item> <item>yeah</item> <item>uh huh</item>

</one-of> </rule>

<rule id=“yes-no”<one-of>

<ruleref uri="#yes"/> <ruleref uri="#no"/>

</one-of></rule>

Other FeaturesOther Featuresoptionalityoptionalitylanguage declarationlanguage declarationweighted alternativesweighted alternativespronunciationspronunciationsspecial rules special rules external rulesexternal rulescharacter encodingcharacter encoding


SRGS SpecificationSRGS Specification

http://www.w3.org/TR/speech-grammar/http://www.w3.org/TR/speech-grammar/ Status: W3C Candidate RecommendationStatus: W3C Candidate Recommendation Quick Guide to the SRGS SpecificationQuick Guide to the SRGS Specification

http://www.conversational-technologies.com/pages/5/http://www.conversational-technologies.com/pages/5/index.htmindex.htm


Semantic InterpretationSemantic Interpretation

Tags are added to the grammar to Tags are added to the grammar to describe the semantics of the user’s describe the semantics of the user’s inputinput

Format uses ECMAScript compact Format uses ECMAScript compact profile (ECMAScript 327)profile (ECMAScript 327)


Semantic Interpretation ExampleSemantic Interpretation Example

Three large pizzas with onions<rule id="pizza"> <ruleref uri="#number"/> <ruleref uri="#foodsize"/> <tag> $.pizzasize=$foodsize;

$.number=$number </tag> pizzas with

<ruleref uri="#tops"/> <tag> $.topping=$tops </tag>

</rule>

Result: pizza.number = 3pizza.pizzasize= “large”pizza.toppings = [“onions”]

XML Result:<pizza> <number> 3 </number> <size> large </size> <toppings> <item> onions </item> </toppings></pizza>


Semantic Interpretation Semantic Interpretation SpecificationSpecification

http://www.w3.org/TR/semantic-interpretatihttp://www.w3.org/TR/semantic-interpretation/on/

Status: W3C Working DraftStatus: W3C Working Draft


EMMAEMMA

Developed by the W3C Multimodal Developed by the W3C Multimodal Interaction Working GroupInteraction Working Group

An XML-based approach to representing An XML-based approach to representing natural language meaningsnatural language meanings

Applicable to multimodal applications, but Applicable to multimodal applications, but originally developed for speechoriginally developed for speech


EMMAEMMA

Represents user inputRepresents user input Vehicle for transmitting user’s intention Vehicle for transmitting user’s intention

throughout applicationthroughout application Focus on language input (text, handwriting, Focus on language input (text, handwriting,

speech)speech) Three componentsThree components

data modeldata model interpretationinterpretation annotation (main focus of standard)annotation (main focus of standard)


InterpretationInterpretation

ExampleExample I want to go from Denver to I want to go from Denver to

PittsburghPittsburgh<instance>

<air_travel>

<from_city>Denver</from_city>

<to_city>Pittsburgh</to_city>

</air_travel>

<instance>

<instance>

<air_travel>

<from_city>Denver</from_city>

<to_city>Pittsburgh</to_city>

</air_travel>

<instance>

<emma:emma emma:version="1.0" xmlns:emma="http://www.w3.org/2003/04/emma#"

</emma:emma>

EMMA ExampleEMMA Example

 <emma:absolute-timestamp emma:start="2003-03-26T0:00:00.15" emma:end="2003-03-26T0:00:00.2"/>

 <rdf:Description rdf:about="#int1" emma:confidence="0.75"/>

<rdf:Description rdf:about="#int1" emma:model="http://myserver/models/city.xml"/>

<emma:interpretation emma:id="int1"> <origin>Boston</origin> <destination>Denver</destination> <date>03112003</date>

</emma:interpretation>

“I want to go from Boston to Denver on March 11, 2003”


EMMA SpecificationEMMA Specification

http://www.w3.org/TR/emmahttp://www.w3.org/TR/emma

Status: W3C Working DraftStatus: W3C Working Draft


Natural Language Natural Language Understanding Understanding

W3C Standards SummaryW3C Standards Summary VoiceXML: define spoken dialogsVoiceXML: define spoken dialogs SRGS: describes allowable sequences of wordsSRGS: describes allowable sequences of words Semantic Interpretation: describes what Semantic Interpretation: describes what

intentions are represented by sequences of intentions are represented by sequences of wordswords

EMMA: represents an interpretation of user’s EMMA: represents an interpretation of user’s inputinput


Summary of Deployed Spoken Summary of Deployed Spoken Dialog SystemsDialog Systems

Form filling applications are by far the Form filling applications are by far the most commonmost common

Statistical classification systems are Statistical classification systems are becoming more common and are popular becoming more common and are popular with userswith users

Standards are accelerating commercial Standards are accelerating commercial adoption of technologyadoption of technology


ResourcesResources Practical Spoken Dialog Systems, Springer, 2005. (D. Dahl, editor)Practical Spoken Dialog Systems, Springer, 2005. (D. Dahl, editor) VB website VB website http://www.w3.org/Voice/http://www.w3.org/Voice/

VoiceXMLVoiceXML SRGSSRGS SISRSISR

MMI website MMI website http://www.w3.org/2002/mmi/http://www.w3.org/2002/mmi/ EMMAEMMA

BeVocal website BeVocal website http://cafe.bevocal.com/http://cafe.bevocal.com/ VoiceXML deployments (some with phone numbers you can try VoiceXML deployments (some with phone numbers you can try

http://www.kenrehor.com/voicexml/#deploymentshttp://www.kenrehor.com/voicexml/#deployments)) Guide to speech standards -- Guide to speech standards --

http://www.speechtechmag.com/issues/9_8/cover/11619-1.htmlhttp://www.speechtechmag.com/issues/9_8/cover/11619-1.html

1 commercial applications of natural language technology april 14, 2005 deborah a. dahl principal,...

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