Dialogue Ling 571 Fei Xia Week 8 111505

Dialogue Ling 571 Fei Xia Week 8: 11/15/05

Outline • Properties of dialogues • Dialogue acts • Dialogue manager

Properties of dialogue • Turn-taking: • Grounding: • Implicature

Turn-taking • Overlapped speech: less than 5%. • Little silence between turns either: The speaker begin motor planning before the previous speaker finishes. • Turn-taking rule: – If the current speaker selects A, A must speak. – If the current speaker does not select the next speaker, any speaker can speak. – If no one else takes the next turn, the current speaker may take the next turn.

Turn-taking (cont) • Adjacency pair: two-part structures – Question / Answer – Greeting / Greeting – Complement / downplayer – Request / Grant • Significant silence: follows 1 st part of an adjacency pair.

Turn and utterance • A single utterance may span several turns: – A: I am leaving tomorrow – B: really? – A: right after my class. • A turn may contain several utterances – A: Do you think it will work? Will John get mad at me? What if Mary does not like it? … – B: Slow down.

Grounding • Speaker and Hearer must establish common ground. • Examples: – Acknowledgement: yeah, that’s great – Demonstration: rephrase or collaboratively completing A’s utterance – Request of repair: Huh? What? – ….

Conversational Implicature • Example: – A: What day in May did you want to travel? – B: I need to be there for a meeting that is from 12 th to 15 th. • Speakers expect hearers to draw certain inferences. • (Grice, 1975) What enables hearers to draw these inferences is that conversation is guided by a set of maxims.

Grice’s four maxims • Maxim of Quantity: Be exactly as informative as is required: – Make your contribution as informative as is required (for the current purposes of the exchange). – Don’t make your contribution more informative than is required. – Ex: There are three flights today.

Grice’s maxims (cont) • Maxim of Quality: try to make your contribution one that is true: – Do not say what you believe to be false. – Do not say that for which you lack adequate evidence • Maxim of Relevance: be relevant. • Maxim of Manner: be perspicuous: – – Avoid obscurity of expression Avoid ambiguity Be brief Be orderly

Grice’s maxims (cont) • • Maxim of Quantity Maxim of Quality Maxim of Relevance Maxim of Manner • Do people actually follow the maxims?

Properties of dialogue • Turn-taking: – – Overlapping speech Little silence Turn-taking rules Utterance segmentation • Grounding: acknowledgement, request of repair • Implicature: four axioms • Case study: Instant messaging

Instant messaging • Turn-taking: – More overlapping speech – Longer silence – Turn-taking rules – More unsynchronized exchange – One more level: utterance, turn, message • Grounding is more important. • Implicature: four axioms • Discourse structure: a tree?

Outline • Properties of dialogues • Dialogue acts • Dialogue manager

Dialogue acts • Austin (1962): three kinds of acts – Locutionary act: the act of uttering a string of words – Illocutionary act (a. k. a. speech act): the act that the speaker performs in the utterance – Perlocutionary act: production of effects by means of the utterance. • Searle (1975): five classes of speech acts

DAMSL architecture • DAMSL: Dialogue Act Markup in Several Layers • Forward looking function: – – – Statement Info-Request (e. g. Check) Influence-on-Addressee Influence-on-speaker (e. g. , Offer, Commit) Conventional (e. g. , Opening, Closing, Thanking)

DAMSL (cont) • Backward looking function: – Agreement: Accept, Reject, Hold – Answer – Understanding: • Signal-non-understanding • Signal-understanding: Ack, Repeat-Rephrase, … • Tag set is hierarchical: e. g. , CHEK is a kind of INFO-REQUEST

An example [assert] [info-req, Ack] [assert, Answer] [info-req, ack] [assert, Answer] C: I need to travel in May. A: And, what day in May do you want to travel? C: I need to be there for a meeting on 15 th. A: And you are flying into what city? C: Seattle.

How to identify these acts? • Examples: – Can you give me a list of flights from A to B? – It’s hot in here. • Two models: – Inference model: symbolic approach – Cue model: statistical approach

Plan inference model • Plan inference (PI) rules: – – • Action-Effect Rule: if action X causes Y, and if H believes that S wants X to be done, then it is plausible that H believes that S wants Y to obtain. …. Ex: “Can you give me a list of flights …. ? 1. X asked me a question about whether I have the ability to give a list of flights. 2. I assume that X is being cooperative in the conversation (in the Gricean sense) and that his utterance has some aim. 3. X knows that I have the ability to give such a list, and there is no alternative reason why X should have a purely theoretical intereset in my list-giving ability.

Can you give me a list of flights …? 4. Therefore X’s utterance probably has some ulterior illocutionary point. What can it be? 5. A preparatory condition for a directive is that the hearer have the ability to perform the directed action. 6. Therefore X has asked me a question about my preparedness for the action of giving X a list of flights. 7. Furthermore, X and I are in a conversational situation in which giving lists of flights is a common and expected activity. 8. Therefore, in the absence of any other plausible illocutionary act, X is probably requesting me to give him a list of flights.

Cue-based model • Cues: – Words and collocation: “Would you” for request – Prosody: rising pitch for “Yes-no question”, stress, energy, …. – Conventional structure: • (1) A: Do you want to see the movie? • (2) B: Yeah. • (1) A: John was very late last night. I was very mad at him, … • (2) B: Yeah.

Cue-based approach (cont) • Treat it as a tagging problem. • Given the evidence E, find a dialogue act sequence D*, such that

Cue-based approach (cont)

Outline • Properties of dialogues • Dialogue acts • Dialogue manager

Dialogue Manager

Where are dialogue systems used? • • Banks, Utility companies, … Airline travel information systems Restaurant guides Telephone interfaces to emails or calendars • Financial services: buy/sell stocks • Technology: Apple, Dell, Microsoft, …

What does a dialogue system do? • “Understand” human’s utterance: – Reference resolution – Dialogue act – Human’s intention • Decide what to do: the intended dialogue act • Produce the utterance

A dialogue system • Homey: Home monitoring through intelligent dialog system • By Advanced Computation Lab (ACL), part of Cancer Research UK, Europe's largest independent cancer research organization. • Demo: local-demo

Dialogue • • • Patient’s Age? 42 -yr and female. Jewish? No. Does the patient have cancer? Yes. – What kind of cancer? Others. • • • Bah bah … is it correct? Yes. Age when diagnosed? 40 yrs Relatives with cancers: her sister has breast cancer, her father’s mom has varying cancer – Father has cancers? No – Father’s sibling: No – Mother has cancer? No, but her mother’s sibling has cancer. • What kind of cancer? Other. – Mother’s parents have cancer? No. • • • Bah bah …, Is it correct? No, she was 41. Bah bah. . . , is it correct? Yes. Two possibilities. Increase risk or population risk. I recommend increase risk. what do you advise? – Why? Give reason. – Is there evidence for population risk? No.

Observation • Can handle extra info: – H: she was 42 and female. • Follow-up questions: – H: Her mother’s sister has cancer – S: what kind of cancer? • Reference solution: – S: …. , is it correct? – H: No, she was 41. – S: . . . , is it correct?

Observation (cont) • Explicit batch confirmation and correction afterwards • Mixed initiative: – S: I recommend …. . What do you advise? – H: Why do you recommend that? – S: Because …. • Inference: – 42 under 45 – father’s mother one of father’s parents

Components of a dialogue system • • Speech recognizer Speech synthesizer Natural language generator IE to fill some templates Pronoun resolution Knowledge base: medical ontology Dialogue manager

Dialog management • Goal: determine how to respond to user utterance: – – – Answer user question Solicit information Confirm/Clarify user utterances Notify invalid answer Suggest alternatives • Interface between user/language processing components and system knowledge base

Initiative strategies • System-initiative: system always has control • User-initiative: user always has control • Mixed-initiative: control switches between user and system.

Confirmation strategies • Human: I want to go to Seattle • Explicit: System: did you say you want to go to Seattle? • Implicit: System: what time did you want to leave Seattle?

An example • Human: I want to go to Pittsburgh in May. • Dialogue manager: – Turn holder: system – Intended speech acts: Req-info, Ack, Clarify – Discourse goal: get-travel-info, create-travelplan • System: And, what day in May did you want to travel?

FSA-based system No What city are you leaving from? Is-city(answer) No Where are you going? Is-city(answer) When would you like to leave? Is-time(answer) No

Template-based approach • Slots – – – – – From_airport: “From what city are you leaving? ” To_airport: “Where are you going? ” Dep_time: “When would you like to leave? ” Arr_time Fare_class Airline One_way Nonstop Preferred_seat Food_preferrence

Evaluation measures • User satisfaction: ask human to fill out questionnaires: – Were answers provided quickly enough? – Was the system easy to use? • Task completion success: maximize success rate – Percent of subtasks completed – Correctness of each utterance • Task completion cost: minimize cost – Completion time in turns or seconds – Number of queries – Number of system non-responses

How good are automated systems? • An article on the Seattle Times (11/6/05): “How to outsmart automated phone systems? ” • Shortcuts and tips: – Go to the website or use “live-chat”. – Sometimes it is faster to not respond when prompted by an automated phone system, or pretend you’re calling from a rotary-dial phone.

Summary of Dialogue • Properties of dialogues: – Turn-taking – Grounding – Implicatures • Dialogue acts – Types of dialogue acts – Identifying dialogue acts: inference-based, cue -based • Dialogue manager