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Pragmatics, conversation and Pragmatics, conversation and the Companions projectthe Companions project

Yorick WilksYorick WilksOxford Internet InstituteOxford Internet InstituteUniversity of SheffieldUniversity of Sheffield

Roberta Roberta CatizoneCatizoneUniversity of SheffieldUniversity of Sheffield

www.companionswww.companions--project.orgproject.org

IasiIasi, 2007, 2007

22

Pragmatics is best grounded in dialogue, not prose, Pragmatics is best grounded in dialogue, not prose, because individualbecause individual--relativity or pointrelativity or point--ofof--view phenomena view phenomena (e.g. speaker (e.g. speaker vsvs. hearer) are more obviously crucial . hearer) are more obviously crucial there.there.This follows from MorrisThis follows from Morris’’ original definition of pragmatics original definition of pragmatics and its relation to conventional notions like and its relation to conventional notions like deixisdeixis..I have argued over 20 years that the role of I have argued over 20 years that the role of ““pointpoint--ofof--viewview”” phenomena is wider and deeper than that: it phenomena is wider and deeper than that: it extends to metaphor, belief and the identification of extends to metaphor, belief and the identification of intensional intensional objects between speakers.objects between speakers.E.g. to understand me, you need to know that I believe E.g. to understand me, you need to know that I believe that that ““FrankFrank”” indicates the same person as indicates the same person as ““MarthaMartha’’s s uncleuncle”” (which is more than semantics, since you may not (which is more than semantics, since you may not actually believe that).actually believe that).

Assumptions of the talkAssumptions of the talk

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BallimBallim, A. and Wilks, Y. 1991. Artificial Believers, Erlbaum, A. and Wilks, Y. 1991. Artificial Believers, Erlbaum–– The VIEWGEN paradigm we shall return to below The VIEWGEN paradigm we shall return to below

BallimBallim, A., Wilks, Y., and , A., Wilks, Y., and BarndenBarnden, J. 1991. Belief , J. 1991. Belief Ascription, Metaphor and Ascription, Metaphor and Intensional Intensional Identification. Identification. Cognitive Science.Cognitive Science.Wilks, Y., Wilks, Y., BarndenBarnden, J., and Wang, J. 1991. Your metaphor , J., and Wang, J. 1991. Your metaphor or mine: belief ascription and metaphor identification. Proc. or mine: belief ascription and metaphor identification. Proc. IJCAI, Sydney. IJCAI, Sydney. Wilks, Y. 1987. Relevance must be to someone. Behavioral Wilks, Y. 1987. Relevance must be to someone. Behavioral and Brain Sciences. and Brain Sciences.

The last reference is relevant because The last reference is relevant because Gazdar Gazdar and I failed and I failed to kill off (soon after birth) an to kill off (soon after birth) an ““objectivistobjectivist”” relevance theory relevance theory without any pointwithout any point--ofof--view aspects!view aspects!The role of computational mechanisms (actual and The role of computational mechanisms (actual and possible) in this talk: the only test of knowing you are possible) in this talk: the only test of knowing you are making sensemaking sense----CL/NLP is not waiting for linguists!CL/NLP is not waiting for linguists!

Some old references saying that:Some old references saying that:

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Meaning is (ultimately) other words [TINA!]Meaning is (ultimately) other words [TINA!]To have meaning is to have one meaning To have meaning is to have one meaning from from among other possible meanings among other possible meanings (I.e. nonsense (I.e. nonsense is usually irredeemably is usually irredeemably ambiguousambiguous))To have a belief is to have a belief To have a belief is to have a belief distinguishable from alternatives (why your distinguishable from alternatives (why your ATM does not have beliefs about your account; ATM does not have beliefs about your account; because it cannot represent anything because it cannot represent anything different/else).different/else).References as earlier and passim at References as earlier and passim at www.www.dcsdcs..shefshef.ac..ac.uk/~yorickuk/~yorick, back to:, back to:Wilks, Y. 1971 Decidability and natural language. Wilks, Y. 1971 Decidability and natural language.

Mind.Mind.

PremissesPremisses----continuity continuity of semantics and of semantics and pragmatics: both are pragmatics: both are ““choicechoice”” phenomenaphenomena

55

Some history of dialogue systems and the difficulty Some history of dialogue systems and the difficulty of incorporating belief pragmatic phenomena within of incorporating belief pragmatic phenomena within them.them.This has been made worse by the shift in NLP to a This has been made worse by the shift in NLP to a statistical methodology since 1990, and the statistical methodology since 1990, and the emphasis on performance emphasis on performance on a large scaleon a large scale..But there is surprising continuity as regards the But there is surprising continuity as regards the opposition of theory and performance.opposition of theory and performance.A fresh generation of machine learningA fresh generation of machine learning--based based systems, and some fresh optimism about linking the systems, and some fresh optimism about linking the empirical and the conceptual within performing empirical and the conceptual within performing systems.systems.

Main points of talkMain points of talk

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DataData--driven dialogue performance systems versus driven dialogue performance systems versus AI/linguistic theoretical systems, separate since AI/linguistic theoretical systems, separate since 1970s1970sThis distinction has mapped, till now, on domain This distinction has mapped, till now, on domain versus general systemsversus general systemsBoth require modeling of pragmatic phenomena like Both require modeling of pragmatic phenomena like belief, and speech acts, but not in the ways first belief, and speech acts, but not in the ways first tried.tried.The continuing problem of embodying such The continuing problem of embodying such phenomena in systems with scale, performance and phenomena in systems with scale, performance and learning.learning.A possible combined approach, illustrated by past A possible combined approach, illustrated by past Sheffield projectsSheffield projectsA development environment: A development environment: CompanionsCompanions----a new a new EU IP project (2006EU IP project (2006--2010)2010)

Dialogue systems: theory and practiceDialogue systems: theory and practice

77

Machine dialogueMachine dialogue: problems with : problems with available available ““theorytheory””

Dialogue the Cinderella of NLP (data? evaluation?)Dialogue the Cinderella of NLP (data? evaluation?)It can be vacuous: It can be vacuous: ‘‘dialogues are systems of turndialogues are systems of turn--takingtaking’’Speech act analysis initially led to implausibly deep levels Speech act analysis initially led to implausibly deep levels of reasoningof reasoning----you donyou don’’t need plans to sell an t need plans to sell an airticketairticket..For some researchers, dialogue theory is still a question For some researchers, dialogue theory is still a question of how best to deploy existing logic or speechof how best to deploy existing logic or speech--based based methods.methods.Much conversation is not task orientated at all, nor does it Much conversation is not task orientated at all, nor does it have plausible informationhave plausible information--states.states.There are apparently competing theories but little or no There are apparently competing theories but little or no evaluation of dialogue systems (compare Information evaluation of dialogue systems (compare Information

ExtractionExtraction----Hobbs)Hobbs)

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Important historical NLP Important historical NLP systems have all the modern systems have all the modern

traits and functionalities in traits and functionalities in miniatureminiature

ColbyColby’’s PARRY (Stanford, 1971)s PARRY (Stanford, 1971)WinogradWinograd’’s s SHRDLU (MIT 1971)SHRDLU (MIT 1971)Grosz partitioned spaces (SRI, 1979)Grosz partitioned spaces (SRI, 1979)PerraultPerrault, Cohen, Allen, Cohen, Allen’’s speech act s speech act system (Toronto, 1977)system (Toronto, 1977)

99

ColbyColby’’s PARRY s PARRY

Perhaps best ever performance, many users, Perhaps best ever performance, many users, robust, but not a normal subject (I.e. a robust, but not a normal subject (I.e. a paranoid)paranoid)primitive individual models, some control of primitive individual models, some control of dialogue process; but it had lots to say!dialogue process; but it had lots to say!Primitive simulation of intentionality, and Primitive simulation of intentionality, and emotion in output choiceemotion in output choicenot syntax analysis but fast pattern matchingnot syntax analysis but fast pattern matchingFar, far, better than ELIZAFar, far, better than ELIZA

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PARRY conversationPARRY conversation

Have you been hospitalized before?Have you been hospitalized before?THIS IS THE FIRST TIMETHIS IS THE FIRST TIMEHow long have you been there?How long have you been there?ABOUT TWO WEEKSABOUT TWO WEEKSAny headaches?Any headaches?MY HEALTH IS FINEMY HEALTH IS FINEAre you have memory difficulties?Are you have memory difficulties?JUST A FEWJUST A FEW

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Does the little block that the hatched pyramid’s support supports support anything black?

Winograd’s SHRDLU 1971

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GroszGrosz’’s s robot domain model robot domain model

Primitive notion of focus based on a Primitive notion of focus based on a structure of partitioned semantic nets structure of partitioned semantic nets (Hendrix)(Hendrix)Unlike PARRY, but like SHRDLU, has Unlike PARRY, but like SHRDLU, has knowledge of a complex domainknowledge of a complex domainOpening and closing of semantic net Opening and closing of semantic net spaces limit respaces limit re--entry to topicsentry to topicsno real performance at allno real performance at all

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Grosz (IJCAI 1979)Grosz (IJCAI 1979)

(Explicit focus):(Explicit focus):S1: The lid is attached to the container with S1: The lid is attached to the container with

1/21/2”” bolts.bolts.R1: Where are the BOLTS?R1: Where are the BOLTS?(Implicit focus):(Implicit focus):S1: Attach the lid to the container.S1: Attach the lid to the container.R1: Where at the BOLTS?R1: Where at the BOLTS?

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PerraultPerrault, Cohen, Allen system , Cohen, Allen system (1977)(1977)

Based on full speech act reasoningBased on full speech act reasoninguser must have one of two goals, meeting or user must have one of two goals, meeting or catching a traincatching a trainPassenger/User: Do you know when the Passenger/User: Do you know when the Windsor train arrives?Windsor train arrives?This is This is labelled labelled as a REQUEST not a as a REQUEST not a REQUESTREQUEST--INFORM (Y/N) because the INFORM (Y/N) because the system knows the user knows it knows!system knows the user knows it knows!

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Perrault Perrault et al.et al.

System has domain knowledge and System has domain knowledge and reasoning powerreasoning powerbut virtually no performancebut virtually no performancewas the first to assign speech act labels to was the first to assign speech act labels to dialogue itemsdialogue itemshas a simple rigid model of nested belief has a simple rigid model of nested belief (unlike (unlike VIEWGENs VIEWGENs realreal--time nesting time nesting computation on demand).computation on demand).

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Fixed Fixed precomputed precomputed nested beliefs: nested beliefs: passengerpassenger’’s view of systems view of system’’s view of s view of

passengerpassenger’’s beliefs.s beliefs.

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19701970’’s division of approaches s division of approaches to machine conversation.to machine conversation.

DomainDomain--dependent systems with coded dependent systems with coded world knowledge and some parsing and world knowledge and some parsing and reasoning reasoning –– VERSUSVERSUSWide shallow systems with little Wide shallow systems with little knowledge and high performanceknowledge and high performancePublished AI academic systems all in first Published AI academic systems all in first groupgroupOnly the second group performed at allOnly the second group performed at all

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AI people join the performers:AI people join the performers:the the Loebner Loebner CompetitionCompetition

Well designed competition over the last Well designed competition over the last twenty yearstwenty years------essentially a modified Turing essentially a modified Turing test.test.Domain dependent entries till 1997, then Domain dependent entries till 1997, then domain free.domain free.The game is for journalists to separate and The game is for journalists to separate and rank human and machine partnersrank human and machine partnersSearch Search ‘‘LoebnerLoebner’’ on web for full transcriptson web for full transcripts

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Loebner Loebner 19941994NEW JUDGENEW JUDGE

150 Judge 1150 Judge 1Do you think the country is headed generally in a more conservatDo you think the country is headed generally in a more conservative or ive or

more liberal direction right now?more liberal direction right now?

151 Terminal151 TerminalDidnDidn’’t you say would you t you say would you discribe discribe that old texas billionairethat old texas billionaire as a as a

conservative?? Answer me one question conservative?? Answer me one question –– why are liberals why are liberals portrayed as portrayed as donkeesdonkees? Are you a caring person? You? Are you a caring person? You’’re not really re not really talking about me, are you?talking about me, are you?

152 Judge 1152 Judge 1II’’ve got the feeling Ive got the feeling I’’ve heard these answers before. Is our nation swinging ve heard these answers before. Is our nation swinging

leftward or rightward right now?leftward or rightward right now?

153153 TerminalTerminalWe are at the beginning of a strong and wonderful economic recoWe are at the beginning of a strong and wonderful economic recovery, very,

and we can thank the liberals!and we can thank the liberals!

2020

Loebner Loebner 19971997PROGRAM[10:18:04]Hi, my name is Catherine, whatPROGRAM[10:18:04]Hi, my name is Catherine, what’’s yours?s yours?JUDGE04[10:18:32]My name is Elliott.JUDGE04[10:18:32]My name is Elliott.PROGRAM[10:18:34]Well, itPROGRAM[10:18:34]Well, it’’s nice to meet you, Elliott.s nice to meet you, Elliott.PROGRAM[10:18:48]Did you see that story on CNN last night about PROGRAM[10:18:48]Did you see that story on CNN last night about the lesbianthe lesbianPROGRAM[10:19:04]couple who came out at a White House party on SPROGRAM[10:19:04]couple who came out at a White House party on Sunday?unday?JUDGE04[10:19:55]Yes, I did. I think it may all be a publicity JUDGE04[10:19:55]Yes, I did. I think it may all be a publicity stunt for Ellen.stunt for Ellen.PROGRAM[10:20:01]Ellen PROGRAM[10:20:01]Ellen Degeneres Degeneres was one of them was one of them –– she was kissing her lovershe was kissing her loverPROGRAM[10:20:11]in front of Bill Clinton. Do you think he was vPROGRAM[10:20:11]in front of Bill Clinton. Do you think he was veryeryPROGRAM[10:20:25]embarrassed?PROGRAM[10:20:25]embarrassed?JUDGE04[10:21:00]I think he want the gay vote. He will do anythiJUDGE04[10:21:00]I think he want the gay vote. He will do anything to get it and ng to get it and

keep it.keep it.JUDGE04[10:21:15]He does not have a conservative base.JUDGE04[10:21:15]He does not have a conservative base.PROGRAM[10:21:21]PROGRAM[10:21:21]PROGRAM[10:21:21]Are you still a Friend of Bill Clinton?PROGRAM[10:21:21]Are you still a Friend of Bill Clinton?

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Sheffield won in 1997!Sheffield won in 1997!

The CONVERSE program pretended to be The CONVERSE program pretended to be Catherine, a 34Catherine, a 34--year old British journalist year old British journalist living in New York (to explain any usage living in New York (to explain any usage problems).problems).She could talk about 80 appropriate topicsShe could talk about 80 appropriate topics(80=domain free???) clothes, shopping, (80=domain free???) clothes, shopping, food, abortion, fitness, sexfood, abortion, fitness, sex…….etc..etc.

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The Sheffield dialogue project: The Sheffield dialogue project: moving performance towards moving performance towards

theorytheory

The The Loebner Loebner Prize Competition Prize Competition www.www.acmacm..org/org/--loebner/loebnerloebner/loebner--prizeprize.html.html

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The CONVERSE prototype 1997The CONVERSE prototype 1997PushPush--meme--pullpull--you architecture you architecture strong driving topstrong driving top--down scripts (80+) in a redown scripts (80+) in a re--enterable network with complex output enterable network with complex output functionsfunctionsbottombottom--up parsing of user input adapted from up parsing of user input adapted from statistical prose parserstatistical prose parserminimal models of individualsminimal models of individualscontained contained Wordnet Wordnet and Collins and Collins PNsPNssome learning from past some learning from past Loebners Loebners + BNC+ BNCLearning from PARRYLearning from PARRY------having something to having something to say.say.Failed to incorporate VIEWGEN belief model Failed to incorporate VIEWGEN belief model (from(from ““Artificial BelieversArtificial Believers””))

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VIEWGEN :a belief model that VIEWGEN :a belief model that computes agentscomputes agents’’ states states

Not a static nested belief structure like that of Not a static nested belief structure like that of Perrault Perrault and Allen.and Allen.Computes other agentsComputes other agents’’ RELEVANT states at time RELEVANT states at time of need by a simple pushdown default of need by a simple pushdown default algoritmalgoritmTopic restricted search for relevant informationTopic restricted search for relevant informationCan represent and maintain conflicting agent Can represent and maintain conflicting agent attitudesattitudesMore defensible than common ground or shared More defensible than common ground or shared belief theories.belief theories.As a model of belief dependent reference: finding As a model of belief dependent reference: finding the ground of: the ground of: ””that old Texas billionairethat old Texas billionaire”” as as Ross Perot, against a background of what the Ross Perot, against a background of what the hearer may assume the speaker knows.hearer may assume the speaker knows.

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system belief

system goal

simon belief

cause(deleted(x,sub-directory)not(happy(david)))

deleted(system,sub-directory)

deleted(system,sub_directory)

Candidate Planning Actionsfor system

I have just deleted the subdirectory

Perform Action

Click here for Plan recognition

Belief representation of the first turn of an exchange: from Mark Lee’s thesis.

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Contrast TRINDI:Contrast TRINDI: the the infomration infomration state update modelstate update model

•• Information state and dialogue Information state and dialogue management in the TRINDI Dialogue Move management in the TRINDI Dialogue Move Engine Toolkit, Larsson and Engine Toolkit, Larsson and Traum Traum 20002000

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The TRINDI systemThe TRINDI systemKey references:Key references:Cooper, R., Larsson, S. 1999, Dialogue Moves and information Cooper, R., Larsson, S. 1999, Dialogue Moves and information States, In Bunt & States, In Bunt & ThijsseThijsse, Proc. 3rd. , Proc. 3rd. InternatInternat. Conf. on . Conf. on Computational Semantics. Computational Semantics. PoesioPoesio, M., Cooper, R., Larsson, S., Matheson, C., and , M., Cooper, R., Larsson, S., Matheson, C., and TraumTraum, D. , D. 1999. Annotating conversations for information state update. In 1999. Annotating conversations for information state update. In Proc. Proc. Amstelogue Amstelogue 99 workshop on semantics and pragmatics of 99 workshop on semantics and pragmatics of dialogue.dialogue.Associated optimization learning: Associated optimization learning: Walker, M., Walker, M., FromerFromer, J., and Narayanan, S. 1998. Learning optimal , J., and Narayanan, S. 1998. Learning optimal dialogue strategies. Proc. COLING 98.dialogue strategies. Proc. COLING 98.

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The TRINDI systemThe TRINDI system

EU Project (1999EU Project (1999--Fusion of influences from Gothenburg, Edinburgh and Fusion of influences from Gothenburg, Edinburgh and California.California.Key notion of Information StateKey notion of Information State----contrasted with more contrasted with more linguistic notion of Dialogue Statelinguistic notion of Dialogue StatePlus notion of Dialogue Move Engine.Plus notion of Dialogue Move Engine.Influences of formal semantics, planning and AI Influences of formal semantics, planning and AI approach to approach to ““knowledge based interpretationknowledge based interpretation”” of of language.language.

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Update and Dialogue rules Update and Dialogue rules Update the information stateUpdate the information stateGenerate the next dialogue utteranceGenerate the next dialogue utteranceWork over representations of Work over representations of ““internal stateinternal state”” of user as of user as well as well as ““objective /sharedobjective /shared”” state.state.Information basically as formal propositions.Information basically as formal propositions.A rule has Applicability conditions (LHS) and effects on A rule has Applicability conditions (LHS) and effects on information state (RHS) if the conditions hold.information state (RHS) if the conditions hold.Question Under Discussion (QUD) can add a Question Under Discussion (QUD) can add a questionquestionif an if an askask move has been performed:move has been performed:

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An update rule, applied after An update rule, applied after ““where do you want to go?where do you want to go?””

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Possible strategies of update Possible strategies of update rule application in the TRINDI rule application in the TRINDI

systemsystem

Apply 1st rule that appliesApply 1st rule that appliesApply all rules till none applyApply all rules till none applyApply rules in specific sequencesApply rules in specific sequencesApply some rules based on probabilities (training of Apply some rules based on probabilities (training of strategies)strategies)Present choices for user choice (in development)Present choices for user choice (in development)

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TRINDI control and processes TRINDI control and processes are conventional.are conventional.

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Dialogue move types (in Dialogue move types (in GoDis GoDis system):system):

AskAskAnswerAnswerRepeatRepeatRequest repeatRequest repeatGreetGreetGoodbyeGoodbyeThanksThanksQuitQuit

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Private and Shared Information Private and Shared Information State: Beliefs, Agendas, Plans, State: Beliefs, Agendas, Plans,

tmp tmp (not yet shared but assumed (not yet shared but assumed shared)shared)

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Problems with information updateProblems with information update[over and above a generally agreed notion that [over and above a generally agreed notion that meaning can be plausibly equated to changes in meaning can be plausibly equated to changes in the hearer caused by an utterance]the hearer caused by an utterance]It is associated with an elementary ruleIt is associated with an elementary rule--based based notion of Dialogue Management, rather than a notion of Dialogue Management, rather than a more structuredmore structured topictopic--based view with larger based view with larger structures that can bestructures that can be paused and repaused and re--entered.entered.The The ““privateprivate--sharedshared”” belief opposition is belief opposition is procedurally implausible; we can only compute procedurally implausible; we can only compute shared beliefs by the inverse default that shared beliefs by the inverse default that all private all private belief is shared unless we know otherwisebelief is shared unless we know otherwise. They . They cannot express or compute this. Remember TINA!cannot express or compute this. Remember TINA!

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Problems with the TRINDI Problems with the TRINDI systemsystem

Hard to write rules (even from annotations) with dialogue flowHard to write rules (even from annotations) with dialogue flowUsers can have plans but plans of the system are implicitUsers can have plans but plans of the system are implicitHard to implement System initiative (what is the role of the staHard to implement System initiative (what is the role of the stack?)ck?)Shared information not a clear notionShared information not a clear notionNo real formal semantics just decorationNo real formal semantics just decorationBeliefs too are only a metaphor for more informationBeliefs too are only a metaphor for more informationMany conversations have no Many conversations have no preknown preknown formal world.formal world.

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Reinforcement learning over Reinforcement learning over such systemssuch systems

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How might this robot learn?How might this robot learn?

Reinforcement learningReinforcement learningLearning is associated with a Learning is associated with a rewardrewardBy optimizing reward, algorithm learns optimal strategyBy optimizing reward, algorithm learns optimal strategyKey assumption: problem can be divided into states, Key assumption: problem can be divided into states, transitions, and actions associated with those transitions transitions, and actions associated with those transitions (you have to have something to associate reward with!)(you have to have something to associate reward with!)A Markov Decision Process!A Markov Decision Process!

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Markov Decision ProcessesMarkov Decision ProcessesState, sState, sActions, aActions, aRewards, rRewards, rTransitions (associated with Transitions (associated with actions)actions)Formalises Formalises problemproblem——when when in state S, what is the utility in state S, what is the utility (reward) for taking a (reward) for taking a particular action, among the particular action, among the choices of actions possible?choices of actions possible?

a1r = -1

s2

s3s4

s1

a1

a2

a1

a2

a2

a1a1

a2

p(0.5)

p(0.5)

r = 5

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Q Learning (Watkins, 1989)Q Learning (Watkins, 1989)

Every action has a Every action has a Q value associated Q value associated with itwith itActions are Actions are explored according explored according to a policy or at to a policy or at random random

S1

a1 Q(s1,a1)

a2 Q(s1,a2)

an Q(s1,an)

......

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RewardsRewards

Once we have taken a transition, we receive Once we have taken a transition, we receive a rewarda reward

S1 S5

a2

r=3.4

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Moving aheadMoving ahead

At this point, we measure the utility of the At this point, we measure the utility of the state by maximizing the Q valuestate by maximizing the Q valueOnce new state is chosen, we update Q Once new state is chosen, we update Q value of preceding state according to value of preceding state according to reward and new statereward and new state’’s Q values Q value

a1 Q(s5,a1)

a2 Q(s5,a2)

an Q(s5,an)

......S5

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Problems with Q learning/RL in Problems with Q learning/RL in generalgeneral

State space can be huge, thereforeState space can be huge, therefore–– Time to search it can be quite longTime to search it can be quite long–– Memory requirements can be quite bigMemory requirements can be quite big–– States can be missed in searchStates can be missed in searchLearner items tend to the obvious which Learner items tend to the obvious which could have been structurally embeddedcould have been structurally embedded

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Applying RL to Spoken Dialogue Systems Applying RL to Spoken Dialogue Systems (Walker, (Walker, ‘‘00)00)

Consider a system providing access to Consider a system providing access to email over the phoneemail over the phoneDialogue strategies:Dialogue strategies:–– Summarize email by senderSummarize email by sender–– Summarize email by subjectSummarize email by subject–– Summarize email by both subject and senderSummarize email by both subject and senderProblem: What is the best summarization Problem: What is the best summarization method?method?

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Applying RL to Spoken Dialogue Systems Applying RL to Spoken Dialogue Systems (Walker, (Walker, ‘‘00)00)

Consider a system providing access to Consider a system providing access to email over the phoneemail over the phoneDialogue strategies:Dialogue strategies:–– Summarize email by senderSummarize email by sender–– Summarize email by subjectSummarize email by subject–– Summarize email by both subject and senderSummarize email by both subject and senderProblem: What is the best summarization Problem: What is the best summarization method?method?

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How do assign costs/reward?How do assign costs/reward?

On utteranceOn utterance--byby--utterance basisutterance basis–– Partial info from user may result in large output from DB Partial info from user may result in large output from DB

queryquery–– Slow and irritating delivery of resultsSlow and irritating delivery of results–– Can be done with user simulationCan be done with user simulation

As function of overall dialogueAs function of overall dialogue–– Task completion (Did user get information?)Task completion (Did user get information?)–– Time to completion (Longer=worse)Time to completion (Longer=worse)–– User satisfaction (Was user happy with interaction?)User satisfaction (Was user happy with interaction?)–– Typically requires real user dataTypically requires real user data

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Assigning overall utilityAssigning overall utility

Best dialogue strategy the one that Best dialogue strategy the one that maximizes utility function, which is itself maximizes utility function, which is itself dependent on reward functiondependent on reward functionChoosing reward function is importantChoosing reward function is importantShould make sense, be easy to measure, Should make sense, be easy to measure, and correlate with some intuition about and correlate with some intuition about improvementimprovementData collection for reinforcement learning Data collection for reinforcement learning always an issuealways an issue

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What do we learn about dialogue What do we learn about dialogue systems?systems?

DonDon’’t greet user and then hang upt greet user and then hang upDonDon’’t present a list of flights after only t present a list of flights after only eliciting destinationeliciting destinationBest constraintBest constraint--elicitation order: source, elicitation order: source, destination, airline, date, timedestination, airline, date, timeShorter summaries for email are preferredShorter summaries for email are preferred

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ASR now a mature statistically based ASR now a mature statistically based technologytechnologyIn other areas of dialogue, statistical In other areas of dialogue, statistical methods less maturemethods less matureA complete dialogue system can be seen as A complete dialogue system can be seen as a Partially Observable Markov processa Partially Observable Markov processSubcomponents can be observed in turn Subcomponents can be observed in turn with intermediate variableswith intermediate variablesSome liaison with TRINDI and Some liaison with TRINDI and Traum Traum systemssystems

Also Steve YoungAlso Steve Young’’s speech driven s speech driven program (2002 onwards):program (2002 onwards):

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YoungYoung’’s s statistical statistical modulesmodules

Speech Speech understandingunderstandingSemantic decoding Semantic decoding Dialogue Dialogue act detectionact detectionDialogue management Dialogue management and and controlcontrolSpeech Speech generationgeneration

I.e. I.e. roughly same roughly same as as everyone elseeveryone else’’ss!!!!

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5252

Strategy Strategy not not like like JelinekJelinek’’s MT s MT strategy strategy of 1989!of 1989!

Which was non/antiWhich was non/anti--linguistic with no linguistic with no intermediate representations hypothesisedintermediate representations hypothesisedYoung assumes rougly the same Young assumes rougly the same intermediate objects as we do but in very intermediate objects as we do but in very simplified forms.simplified forms.The aim to to obtain training data for all of The aim to to obtain training data for all of them so the whole process becomes a them so the whole process becomes a single throughput Markov model.single throughput Markov model.

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Young Young concedes this concedes this model model may may only be only be for simple for simple domainsdomains

His domain is a pizza ordering systemHis domain is a pizza ordering systemA typical DialogueAct+Semantics could A typical DialogueAct+Semantics could therefore be:therefore be:–– Purchase_Request{qty=2;topping=pepperoni}Purchase_Request{qty=2;topping=pepperoni}

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Introduction of concepts as Introduction of concepts as well well as as DAsDAs

«« it is convenient to introduce another intermediate it is convenient to introduce another intermediate representationrepresentation…….C represents the set of semantic .C represents the set of semantic concepts encoded within Wconcepts encoded within W »»So P(Au|W,Bs)=So P(Au|W,Bs)=∑P(Au|C,Bs)P(C|W,Bs)

c

I.e.training for concepts given words+statesI.e.training for concepts given words+statesAnd Dialogue acts given concepts+states.And Dialogue acts given concepts+states.

Experience suggsts both can be trained directly on the Experience suggsts both can be trained directly on the words!words!

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Dialogue Dialogue act detectionact detection

Range of methods (including MDL) for Range of methods (including MDL) for solving argmax P(Au|C,Bs)solving argmax P(Au|C,Bs)Interesting that this is not thought to depend Interesting that this is not thought to depend on the WORDS (as most people do it)on the WORDS (as most people do it)No reference to linguistic methods for this No reference to linguistic methods for this (since Samuels et al 98).(since Samuels et al 98).

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Dialogue managementDialogue managementThis This is the is the one one where it is where it is hard to hard to see see how how he can get nonhe can get non--trivial trivial data.data.Data Data can be seen can be seen as a transition as a transition matrix matrix of system states S of system states S against against system actions As, system actions As, filling the matrix cells with filling the matrix cells with new new system states.system states.Model of training Model of training is reinforcement learning ascribed is reinforcement learning ascribed to to Pierracini and then WalkerPierracini and then Walker..No No evidence evidence of of what such what such training changes in practice for a training changes in practice for a nonnon--trivial trivial systemsystem«« the typical the typical system S system S will typically be intractably will typically be intractably large large and and must must be be approiximatedapproiximated »»Puzzle:Puzzle: »»the userthe user’’s beliefs cannot be directly observed and s beliefs cannot be directly observed and must must therefore be inferredtherefore be inferred »» truetrue, but, but……………………..

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Questions about Questions about the the Speech Speech program program for dialoguefor dialogue

Is this just a description of the empirical NLP Is this just a description of the empirical NLP program of work or an attempted reduction like:program of work or an attempted reduction like:–– JelinekJelinek’’s MT without linguistss MT without linguists–– PollackPollack’’s RAAM for (Fodors RAAM for (Fodor’’s) syntactic recursions) syntactic recursion

Can data be found to reduce all of a (fairly general) Can data be found to reduce all of a (fairly general) DM to a transition space that can be reward trained?DM to a transition space that can be reward trained?What is the real effect of training a DMWhat is the real effect of training a DM----however however found? Reducing unused paths?found? Reducing unused paths?How in principle express changes to planning and How in principle express changes to planning and belief spaces?belief spaces?

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Dialogue managementDialogue managementThis This is the is the one one where it is where it is hard to hard to see see how how he can he can get nonget non--trivial trivial data.data.Data Data can be seen can be seen as a transition as a transition matrix matrix of system of system states S states S against against system actions As, system actions As, filling the matrix filling the matrix cells with cells with new system states.new system states.Model of training Model of training is reinforcement learning ascribed is reinforcement learning ascribed to to Pierracini and then WalkerPierracini and then Walker..No No evidence evidence of of what such what such training changes in training changes in practice for a practice for a nonnon--trivial trivial systemsystem«« the typical the typical system S system S will typically be intractably will typically be intractably large large and and must must be approiximatedbe approiximated »»Puzzle:Puzzle: »»the userthe user’’s beliefs cannot be directly observed and s beliefs cannot be directly observed and must must therefore be inferredtherefore be inferred »» truetrue, but, but……………………..

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Moving academic systems Moving academic systems towards performancetowards performance

Best known paradigm is Best known paradigm is TraumTraum’’s s TRAINS systemTRAINS system----descendant of Allendescendant of Allen’’s s work (Torontowork (Toronto--Rochester tradition)Rochester tradition)uses corpora but retains some reasoning uses corpora but retains some reasoning moved to the army+movies institute in moved to the army+movies institute in California!California!The Sheffield goal is moving The Sheffield goal is moving performance systems towards theory!!!performance systems towards theory!!!

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Trains World Map

System’s Map of the TRAINS World

Bath

ElmiraAvon

Dansville

Corning

2 BoxcarsAvailable

OrangeWarehouse

3 TankersAvailable

Engine E2Engine E32 BoxcarsAvailable

3 BoxcarsAvailable

Engine E1

BananaWarehouse

OJ Factory

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TRAINS CORPUSTRAINS CORPUSDavid David Traum Traum (Rochester)(Rochester)

utt1 : s: hello <utt1 : s: hello <silsil> can I help you> can I help youutt2 : u: yeah I want tutt2 : u: yeah I want t-- I want to determine the maximum number of I want to determine the maximum number of

boxcars of oranges <boxcars of oranges <silsil> by seven a.m. <> by seven a.m. <silsil> tomorrow morning> tomorrow morningutt3 : so <utt3 : so <brthbrth> hm <> hm <silsil>>

so I guess all the boxcars will have to go through oranso I guess all the boxcars will have to go through oran-- <<silsil> through > through Corning because thatCorning because that’’s where the orange juice <s where the orange juice <brthbrth> orange factory is> orange factory is

utt4 : so from Corning to Bath how far is thatutt4 : so from Corning to Bath how far is thatutt5 : s: two hoursutt5 : s: two hoursutt6 : u: and itutt6 : u: and it’’s s gonna gonna take us also an hour to load <take us also an hour to load <silsil> boxcars right> boxcars rightutt7 : s: right + +utt7 : s: right + +utt8 : u: + okay + so <utt8 : u: + okay + so <silsil> hm so <> hm so <silsil> every trip will take at least <> every trip will take at least <silsil> >

three hours <three hours <silsil> then> thenutt9 : umutt9 : umutt10 : s: right we can unload any amount of cargo onto a train utt10 : s: right we can unload any amount of cargo onto a train in one hourin one hourutt11 : so we can + <utt11 : so we can + <silsil> so a maximum of three + boxcars in an hour> so a maximum of three + boxcars in an hour

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Meanwhile, the world had moved Meanwhile, the world had moved on and NLP empiricism had on and NLP empiricism had

reached dialogue processing: reached dialogue processing: Dialogue Act tagging of utterancesDialogue Act tagging of utterancesDialogue actDialogue act--toto--utterance learning using utterance learning using machine learning over machine learning over nn--grams grams and preceding and preceding dialogue acts (dialogue acts (CfCf. Samuels et al. 1998). Samuels et al. 1998)Speech act sequence statistics from Speech act sequence statistics from Verbmobil Verbmobil ((CfCf. Maier & . Maier & ReithingerReithinger) ) Longman BNC dialogue Longman BNC dialogue ngrams ngrams (50% all (50% all domains are domains are ““fillerfiller””))

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Sheffield Sheffield program program for a for a hybrid enginehybrid engine----as as much much ML as possible: ML as possible: starting with starting with a a

naive naive classifier for DA sclassifier for DA s

WebbWebb’’s s thesis work thesis work in 5FP AMITIES in 5FP AMITIES projectproject..Direct Direct predictivity predictivity of DA s by of DA s by nn--grams grams as a as a preprocess preprocess to to any any ML ML algorithmalgorithm. . Get Get P(d|n) for all 1P(d|n) for all 1--4 4 word nword n--grams and the grams and the DA set DA set over the Switchboard over the Switchboard corpus, corpus, and take and take DA DA indicated indicated by by nn--gram with highest predictivity gram with highest predictivity ((threshold threshold for for probability levelsprobability levels))Do 10Do 10--foldcross validation (foldcross validation (which lowers which lowers scores)scores)Gives Gives 63% 63% over Switchboard over Switchboard but but using only using only a a fraction of fraction of the the data data Stolcke neededStolcke needed, 75% on , 75% on better better data.data.

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Sheffield Sheffield program program for a for a hybrid enginehybrid engine----as as much much ML as possible: ML as possible: then then a Dialogue a Dialogue Manager Manager from the from the 5FP COMIC 5FP COMIC project project

with with ML ML addedadded

Structures to capture large Structures to capture large scale scale dialogue dialogue phenomenaphenomena: : topicstopics, suspension of , suspension of topicstopics, mixed initiative, mixed initiativeDialogue Action Dialogue Action Frames Frames ((DAFsDAFs) ) ----based based on on ATNs used ATNs used in in parsingparsing----allow any allow any actions on transitionsactions on transitionsRetrieved and placed Retrieved and placed on a simple on a simple stackstack----top top DAF DAF is is one one operatedoperated..SystemSystem initiative initiative modelled modelled by by preloaded DAFs preloaded DAFs on on stackstackExploration of Exploration of rere--entry entry to to topics topics by by «« dumpingdumping »»uninstantiated unreuninstantiated unre--entered stacked DAFsentered stacked DAFs..Contemporaray Contemporaray ofof WITAS (WITAS (Lemon Lemon & Peters) & Peters) at Stanford at Stanford

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DAF exampleDAF example

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DAM DAM -- ImplementationImplementationThe core mechanism for the DAM is a simple popThe core mechanism for the DAM is a simple pop--push stack push stack (slightly augmented), onto which structures are loaded and run (slightly augmented), onto which structures are loaded and run ((cfcf. WITAS system at Stanford, Lemon and Peters).. WITAS system at Stanford, Lemon and Peters).

The structures are Dialogue Action Forms (The structures are Dialogue Action Forms (DAFsDAFs), which are ), which are implemented as Augmented Transition Networks (implemented as Augmented Transition Networks (ATNsATNs).).

–– ATNs ATNs are transition networks that can function at any level are transition networks that can function at any level from finite state machines to Turing Machine power.from finite state machines to Turing Machine power.

The stack is preThe stack is pre--loaded with loaded with DAFs DAFs that satisfy the overall goal of that satisfy the overall goal of designing a bathroom in the COMIC system.designing a bathroom in the COMIC system.

The control structure has a preference for continuing to run theThe control structure has a preference for continuing to run theDAFs DAFs on the stack (system initiative) unless the user input is on the stack (system initiative) unless the user input is outside the scope of the DAF (user initiative). In which case, outside the scope of the DAF (user initiative). In which case, the the control structure pushes onto the stack the new DAF that most control structure pushes onto the stack the new DAF that most closely matches the user input.closely matches the user input.

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Dialogue Management Dialogue Management DAFs DAFs model the individual topics and conversational model the individual topics and conversational manoeuvres manoeuvres in in the application domain..the application domain..

The stack structure will be preloaded with those The stack structure will be preloaded with those DAFs DAFs which are which are necessary for the COMIC bathroom design task and the dialogue necessary for the COMIC bathroom design task and the dialogue ends when the Goodbye DAF is poppedends when the Goodbye DAF is popped----both both inds inds of initiative of initiative covered.covered.

DAFs DAFs and stack interpreters together control the flow of the dialogueand stack interpreters together control the flow of the dialogue

Suspended Suspended DAFs DAFs can be reentered in a natural way when they can be reentered in a natural way when they reappear at the top of the stack.reappear at the top of the stack.

Good-bye DAF

Greeting DAFRoom measurement DAF

Style DAF…

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Why the Why the dialogue dialogue task is still task is still hardhard«« Where am Where am II »» in in the conversation=what is being the conversation=what is being talked talked about about nownow, , what what do do they wantthey want??Does topic stereotopy Does topic stereotopy help or are help or are just Finitejust Finite--State State pairs pairs enough enough ((VoiceXMLVoiceXML? TRINDIKIT?)?? TRINDIKIT?)?How to How to gather the beliefs/knowledge required gather the beliefs/knowledge required , , preferably from existing preferably from existing sources?sources?Are Are there there distinctive distinctive procedures procedures for for managing managing conversations conversations that can be modelled that can be modelled by by simpler virtual simpler virtual machines (machines (Bayesian Bayesian networks)? (networks)? (=Companions =Companions phase phase IIII------Steve Pulman at Steve Pulman at Oxford)Oxford)Could such Could such networks networks cover cover NLP NLP and and speech but speech but from from the other endthe other end, as , as it wereit were..How to How to learn the learn the dialogue management structures dialogue management structures we we needneed----assuming we doassuming we do----from largefrom large--scale scale data data annotation?annotation?

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Learning Learning to segment dialogue to segment dialogue corporacorpora

Segmenting the corpora we Segmenting the corpora we have have with with a range of Hearst a range of Hearst tilingtiling--style algorithms style algorithms ((initially initially by by topictopic))To segment To segment it plausiblyit plausibly, , hopefully into hopefully into segments segments that that correspond to structures for DM (correspond to structures for DM (DAFs DAFs or Dialogue Action or Dialogue Action Frames Frames in in our namingour naming))Being done Being done on on the annotated the annotated corpus (i.e. a corpus corpus (i.e. a corpus word word model) model) and and on on the the corpus corpus annotated annotated by Information by Information Extraction Extraction semantic semantic tags (a tags (a semantic semantic model of model of the the corpus)corpus)Repetitions Repetitions of DA+of DA+semantics template semantics template patterns patterns amy may amy may a a MDL MDL packing packing of of the the corpus possible as an alternative corpus possible as an alternative segmenting methodsegmenting method..

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There There are are now at now at least four least four competing approachescompeting approaches

Simple Simple handcoded finite handcoded finite state state systems systems in in VoiceXML VoiceXML ((Chatbots and Chatbots and commercial commercial systemssystems))LogicLogic--based systems with reasoning based systems with reasoning e.g. e.g.

TRINDI/Information state update.TRINDI/Information state update.Extensions of speech engineering Extensions of speech engineering methodsmethods, , machine machine learning and very learning and very simple simple intermediate intermediate structure. E.g. SJ Young structure. E.g. SJ Young at at Cambridge. Cambridge. Rational/procedural hybrids based Rational/procedural hybrids based on dialogue on dialogue management structures management structures and and machine machine learning learning e.g. e.g. what we want what we want to do to do

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CompanionsCompanions

Framework 6 Integrated Project Framework 6 Integrated Project MultiMulti--modal Interfacesmodal Interfaces

(2006(2006--2010)2010)15 partners, 12 15 partners, 12 meuromeuro

Coordinator: Coordinator: Yorick Wilks, University of Sheffield Yorick Wilks, University of Sheffield (UK)(UK)

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Partners: Partners: University of Sheffield(UK), France Telecom(FR), University of TUniversity of Sheffield(UK), France Telecom(FR), University of Teesside (UK). Charles eesside (UK). Charles University(CZ), University of University(CZ), University of TampereTampere, Napier University(UK), University of Oxford, Swedish , Napier University(UK), University of Oxford, Swedish Institute of Computer Science(SE), As An Angel(FR), Institute of Computer Science(SE), As An Angel(FR), Loquendo Loquendo (IT), (IT), TelefonicaTelefonica(ES), University (ES), University of Western Bohemia (CZ), University of Washington (US)of Western Bohemia (CZ), University of Washington (US)

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are needed to see this picture.

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A series of intelligent and A series of intelligent and sociable COMPANIONSsociable COMPANIONS

The The SeniorCompanionSeniorCompanion–– The EU will have more and more old people who find The EU will have more and more old people who find

technological life hard to handle, but will have access to technological life hard to handle, but will have access to fundsfunds

–– The SC could sit beside you on the sofa but be easy to The SC could sit beside you on the sofa but be easy to carry aboutcarry about----like a furry handbaglike a furry handbag----not a robotnot a robot

–– It will explain the plots of TV programs and help choose It will explain the plots of TV programs and help choose them for youthem for you

–– It will know you and what you like and donIt will know you and what you like and don’’tt–– It wills send your messages, make calls and summon It wills send your messages, make calls and summon

emergency helpemergency help–– It will read you the news or tell a joke when It will read you the news or tell a joke when youre youre boredbored

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The The Senior Senior Companion is Companion is a a major major technical and technical and social social

challengechallengeIt could represent old It could represent old people as people as their their agents agents and and help in help in difficult difficult situations e.g. situations e.g. with with landlords, or landlords, or guess when guess when to to summon human summon human assistanceassistanceIt could debrief It could debrief an an elderly elderly user about user about events and memories events and memories in in their livestheir livesIt could aid them It could aid them to organise to organise their their lifelife--memories memories (this (this is now is now hard!)(hard!)(see Lifelog and Memories see Lifelog and Memories for Life)for Life)It would be It would be a a repository repository for relatives for relatives laterlaterHas Has «« Loebner Loebner chat aspectschat aspects »» as as well well as informationas information----it is it is to to divertdivert, , like like a pet, not a pet, not just informjust informIt is It is a persistent a persistent and personal and personal social agent social agent interfacing with interfacing with Semantic Semantic Web agentsWeb agents

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Early evidence Early evidence of of the acceptability the acceptability of of this kind this kind of companionof companion

Remember TamagochiRemember Tamagochi??Quite Quite intelligent people intelligent people rushed rushed home to home to feed feed one one ((and later Furbyand later Furby) ) even though they knew it was even though they knew it was a a simple simple empty mechnaismempty mechnaism..And Tamaogochi could And Tamaogochi could not not even even talk!talk!People People with with pets live longer.pets live longer.WouldnWouldn’’t you like t you like a warm pet to a warm pet to remind you what remind you what happened happened in in the the last last episode episode of a TV serial?of a TV serial?OK, OK, you would you would not, but not, but perhaps perhaps millions of millions of your your compatriots wouldcompatriots would?!?!

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An exampleAn example

Mrs JonesMrs Jones–– Has lived a quiet lifeHas lived a quiet life–– But has many photos, now digitised, or relatives she can But has many photos, now digitised, or relatives she can

hardly rememberhardly remember–– Will discussing her photographs help her recover a Will discussing her photographs help her recover a

coherent narrative of her life?coherent narrative of her life?How to annotate? What are the places? Who are the people?How to annotate? What are the places? Who are the people?How to organise?How to organise?How to tell her story?How to tell her story?

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Why we shall need complex theories for even Why we shall need complex theories for even this much functionality: at least beliefsthis much functionality: at least beliefs

Companion: Who is that with you in the Companion: Who is that with you in the Clacton Clacton picture?picture?MrsMrs. Jones: That. Jones: That’’s my s my neighbour Mrsneighbour Mrs. Bland. BlandCompanion: But you told me it was your Aunt Jane when we talked Companion: But you told me it was your Aunt Jane when we talked before, because you always went to before, because you always went to Clacton Clacton with her. And your with her. And your daughter told me it was her too.daughter told me it was her too.Mrs Mrs Jones: Oh dearJones: Oh dear------how forgetful I am.how forgetful I am.

THIS CANNOT BE UNDERSTOOD WITHOUT SOME THIS CANNOT BE UNDERSTOOD WITHOUT SOME SYSTEM OF REASONING AND POINTS OF VIEW.SYSTEM OF REASONING AND POINTS OF VIEW.

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Image AnnotationImage AnnotationDialogue and OntologyDialogue and Ontology--based based annotation of either whole or part of annotation of either whole or part of imagesimages

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The issue of platforms:The issue of platforms:Handhelds? (Health and Fitness)Handhelds? (Health and Fitness)

Women with big hair?Women with big hair?Rabbits with Rabbits with colour colour and mobile ears?and mobile ears?

(NABAZTAG)(NABAZTAG)

http://www.http://www.asanangelasanangel..fr/morgan/fr/morgan/

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M1: Is that Stefan on the left?F1: It is, yes.M1: Great. Is there anything else you would like to say about them?

Senior Companion Scenario

reminisce

chat

recount stories

Annotations/tags

Life narrativePhoto organization

User modelResults

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Cognitive Model

User Model

The technical elements mentioned earlier will be part of this demonstr-ator based on Sheffield and Albany NY

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Health & Fitness CompanionHealth & Fitness Companion

Scenarios and PlatformsScenarios and Platforms

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Because you are going toexercise today, you shouldprepare a pizza for lunch.

CognitiveModels(TEES)

So, you had aseafood pizza forlunch, now youneed to burn thosecalories!

Okay, maybe Ishould run today...Any suggestions?

Indeed, that healthyand delicious pizzareally helped me tomake a better run!

You are doing fine,having a propermeal seems to help.However, you arestill facing thetough part...

Scenarios and Platforms

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One final question in all this:One final question in all this:

Can we overcome the paradox of AI designCan we overcome the paradox of AI design and and rapid prototyping?rapid prototyping?–– Waiting till the end for an implementation means one is Waiting till the end for an implementation means one is

nevernever ready, formal problems and specification always ready, formal problems and specification always continue indefinitely, versuscontinue indefinitely, versus

–– Early implementations tend not to be abandonedEarly implementations tend not to be abandoned----earlyearlyand inadequate decisions tend to stickand inadequate decisions tend to stick

We want to do both at once (Phases I and II)We want to do both at once (Phases I and II)We are using the early Phase I implementations to We are using the early Phase I implementations to provide more data for the Phase II learningprovide more data for the Phase II learning

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Conclusion:Conclusion:

A mere A mere promisory promisory notenoteCan some form of pointCan some form of point--ofof--view structure finally be view structure finally be implemented on a substantial scale in a dialogue implemented on a substantial scale in a dialogue system through machine learning?system through machine learning?Ask again in 2010Ask again in 2010BUT we MUST go for scaleBUT we MUST go for scale--------mere theory cannot and mere theory cannot and will not yield dialogue systemswill not yield dialogue systems----it never has in 40 it never has in 40 years.years.The The ““enemyenemy”” is the pretension of speech engineers to is the pretension of speech engineers to cover all the space of linguistics, belief and knowledge cover all the space of linguistics, belief and knowledge of the world and of dialogue management!of the world and of dialogue management!

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Akino and Primo Puel