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SpeechLogic & NISLab CSLP 2004 2004–09–02 Representing Act–Topic–based Dialogue Phenomena Hans Dybkjær SpeechLogic™, Prolog Development Center A/S & Laila.

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Presentation on theme: "SpeechLogic & NISLab CSLP 2004 2004–09–02 Representing Act–Topic–based Dialogue Phenomena Hans Dybkjær SpeechLogic™, Prolog Development Center A/S & Laila."— Presentation transcript:

1 SpeechLogic & NISLab CSLP –09–02 Representing Act–Topic–based Dialogue Phenomena Hans Dybkjær SpeechLogic™, Prolog Development Center A/S & Laila Dybkjær NISLab, University of Southern Denmark

2 SpeechLogic & NISLab CSLP –09–02 System-user dialogue S 0.1 – PAUSE1000 – Welcome to FerieKonto’s automatic guide. – You may let the system suggest topics or you can try your own words. – You select topics by speaking to the system. – Choose between: – PAUSE200 – 'employee'. – PAUSE200 – ’On leave'. – PAUSE200 – 'live abroad'. – PAUSE200 – Or 'stopped working' due to 'age' or 'health'. – PAUSE500 – Say a topic, say ’repeat' or ask for 'more options'? U 0.1 I’m a student S 1.1 – Did you ask for – Main menu U 1.1 student S 2.1 – If you are a student with SU, you may get your holiday allowance without taking leave from the studies. – But if you work besides the studies, you must take vacation at your work before you can get allowance. S 2.2 – If you have work, it is your employer who must sign your FerieKonto certificate, otherwise it is the social authorities in your city. S 2.3 – PAUSE500 (Opening) Start Success (Offer) (Check) (Repair) (Information) (Request) (Information) A typical, system driven dialogue with meta-communication

3 SpeechLogic & NISLab CSLP –09–02 Dialogue, continued S 2.4 – Do you want to ask for more? U 2.1 must I write the saturdays S 3.1 – From the holiday year 2002 that starts 1 May, the vacation week is changed to 5 vacation days, and saturdays are thus no longer counted as part of the vacation. S 3.2 – Would you like to know more about the 'holiday year'? U 3.1 no thanks goodbye S 4.1 – Goodbye and thank you for calling us. Event 1 nothing recorded Event 2 disconnect(Offer) Start Success (Request) (Information) (Offer) (Reject + request) (Feedback) (Other) (Other) Success Start FAQ conversation has many small transactions

4 SpeechLogic & NISLab CSLP –09–02 Automating annotation Key concerns for commercial spoken dialogue systems (SDSs): –High transaction success rate –Smooth dialogue Very time consuming and costly to measure manually Manual annotation more or less only possibility today Two–step approach towards automatic annotation 1.Annotate utterances with a basic act–topic structure –Can be automated using a parser 2.Transform act–topic patterns into transaction segments –Using rule engine Automation only way to serious statistics

5 SpeechLogic & NISLab CSLP –09–02 First step: annotation.u:.inform {T.student} “I am a student".s:.inform {T.student, T.payment,} “If you are a student living from a grant you may get your holiday allowance while still studying. If you also work you need to take holiday to get your money.” ….s:.inform {T.more} “Is there anything else you want to ask about?".u:.accept {} “Yes” … Context free annotation, simplistic, Move = Act {Topic*} Turn = Move+ (of same speaker)

6 SpeechLogic & NISLab CSLP –09–02 Second step: Transformation Apply act–topic rules to step one annotated dialogues Transform basic acts into composite acts rule select1 Ts_a <– _x:.inform Ts_a _y:.accept {} where _x != _y end {} <– select1.s:.inform {}.u:.accept {}.s:.inform {} "Payment in general“.u:.accept {} "Yes" Formalism designed for the problem

7 SpeechLogic & NISLab CSLP –09–02 Rule examples fromplace < place rule selectSub1 {V_a} <– _x:.inform {N_a} _y:.inform {V_b} where _a < _b _x != _y end rule rule success1 _y:.success {N_b} <– {T_b} _y:.inform Vs_a where _b in Vs_a _x != _y end rule.s: inform {N.fromplace} “Where does the travel start”.u: inform {} “Copenhagen”.s: inform {V.fromplace}.s: inform {N.toplace} “From Copenhagen, where do you want to go” Exchanges and transactions belong to different levels

8 SpeechLogic & NISLab CSLP –09–02 Dialogue phenomena investigated Different acts, different topics, and different speakers Rejecting topics Differentiating topic names and topic values Sets of topics Patterns across turns (move sets) not using all moves in that turn Topic relation: (sub-topic) IS–A (topic) Meta–communication (repair, clarification,...) Multi–level rules: Some rules only apply after match by other rules Summarising feedback Only structural phenomena – propositional contents not considered

9 SpeechLogic & NISLab CSLP –09–02 Status and next steps Restriction: Task oriented human–computer spoken dialogues Declarative rewrite rules with constraints Customised language close to dialogue analysis domain Smoothness criteria not clearly defined –How does smoothness affect transaction counts? To obtain a fully automated annotation process –Parse dialogues to produce basic act–topic annotation –Combine into automatic batch system Need for evaluation of method –Test on larger number of dialogues of different kinds –Establish human coder baseline and compare Both theoretical work and practical tools needed

10 SpeechLogic & NISLab CSLP –09–02 Supplementary slides Warning: you are over time!

11 SpeechLogic & NISLab CSLP –09–02 Background Over–the–phone FAQ SDS on holiday allowance General (non–person related) questions, e.g. –is Saturday considered a holiday? 2700 lines of grammar, 800 (full) words in vocabulary 85 semantic concepts in input, 100+ stories in output Contractual minimum transaction success rate, but –transaction not clearly defined –no baseline from human–human dialogues Approach to measurement: –Transactions defined in terms of patterns of act–topics –Manually: 225 test and 217 production system dialogues –Created web–based manual annotation tool Complex domain but simple tasks

12 SpeechLogic & NISLab CSLP –09–02 Problem in only identifying topics Basically only distinguish between two composite acts: –select: continue with same topic –request: change to new topic So cannot distinguish success and failure No success.u:.inform {} “Your phone number?“.s:.inform {} “Phone number" Success.u:.inform {} “Your phone number?“.s:.inform {} “Phone " More distinction needed

13 SpeechLogic & NISLab CSLP –09–02 Name and value Solution: distinguish –topic name N: the mentioning of a topic –topic value V: details about a topic.u:.inform {} “Your phone number?“.s:.inform {} “Phone number".u:.inform {} “Your phone number?“.s:.inform {} “Phone " Simple yet powerful distinction – may still be parseable

14 SpeechLogic & NISLab CSLP –09–02 More rule examples rule answer _y:.request {N_b} _x:.inform Vs_a <– _y:.inform {N_b} _x:.inform {V_b} where _x != _y end rule rule success3 _y:.success {N_b} <– _x:.request {N_b} _y:.inform Vs_a where _b in Vs_a _x != _y end rule Many variations possible

15 SpeechLogic & NISLab CSLP –09–02 Transaction success Dialogue level task completion? –Works if task is well–defined and goal state clear But many independent tasks –So no single clear goal state –Need to define transaction at sub–task level What constitutes a transaction? –Initiation and conclusion? –Is miscommunication part of a transaction? –What about sub–tasks? Sub–task level transactions may also inform on which parts of the system may be problematic Start and end does not tell about dialogue smoothness Provide users with required information

16 SpeechLogic & NISLab CSLP –09–02 Smooth dialogues More precise overview of problems and their causes and seriousness –Same topic may have fail and success in same call –Few or many repairs –distinction between unwanted and erroneous information –erroneous information is unacceptable (tomorrow is Friday, phone ) –other information than asked for may be more or less serious (fax instead of phone, fax instead of ) –misunderstanding a yes for a no is usually not so serious (repairable) but can be a nuisance –Misrecognitions –Information blocks may contain more than asked for You are way beyond your time frame!

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