LINGUIST 180: Introduction to Computational Linguistics: LINGUIST 180: Introduction to Computational Linguistics Dan Jurafsky Lecture 3: Dialogue and Conversational Agents (part 1)


Outline: Outline The Linguistics of Conversation Basic Conversational Agents ASR NLU Generation Dialogue Manager Dialogue Manager Design Finite State Frame-based Initiative: User, System, Mixed VoiceXML Information-State Dialogue-Act Detection Dialogue-Act Generation


Conversational Agents: Conversational Agents AKA: Spoken Language Systems Dialogue Systems Speech Dialogue Systems Applications: Travel arrangements (Amtrak, United airlines) Telephone call routing Tutoring Communicating with robots Anything with limited screen/keyboard


A travel dialog: Communicator: A travel dialog: Communicator


Call routing: ATT HMIHY: Call routing: ATT HMIHY


A tutorial dialogue: ITSPOKE: A tutorial dialogue: ITSPOKE


Linguistics of Human Conversation: Linguistics of Human Conversation Turn-taking Speech Acts Grounding Conversational Structure Implicature


Turn-taking: Turn-taking Dialogue is characterized by turn-taking. A: B: A: B: … Resource allocation problem: How do speakers know when to take the floor? Total amount of overlap relatively small (5% - Levinson 1983) Don’t pause either Must be a way to know who should talk and when.


Turn-taking rules: Turn-taking rules At each transition-relevance place of each turn: a. If during this turn the current speaker has selected B as the next speaker then B must speak next. b. If the current speaker does not select the next speaker, any other speaker may take the next turn. c. If no one else takes the next turn, the current speaker may take the next turn.


Implications of subrule a: Implications of subrule a For some utterances the current speaker selects the next speaker Adjacency pairs Question/answer Greeting/greeting Compliment/downplayer Request/grant Silence between 2 parts of adjacency pair is different than silence after A: Is there something bothering you or not? (1.0) A: Yes or no? (1.5) A: Eh B: No.


Speech Acts: Speech Acts Austin (1962): An utterance is a kind of action Clear case: performatives I name this ship the Titanic I second that motion I bet you five dollars it will snow tomorrow Performative verbs (name, second) Austin’s idea: not just these verbs


Each utterance is 3 acts: Each utterance is 3 acts Locutionary act: the utterance of a sentence with a particular meaning Illocutionary act: the act of asking, answering, promising, etc., in uttering a sentence. Perlocutionary act: the (often intentional) production of certain effects upon the thoughts, feelings, or actions of addressee in uttering a sentence.


Locutionary and illocutionary: Locutionary and illocutionary “You can’t do that!” Illocutionary force: Protesting Perlocutionary force: Intent to annoy addressee Intent to stop addressee from doing something


The 3 levels of act revisited: The 3 levels of act revisited


Illocutionary Acts: Illocutionary Acts What are they?


5 classes of speech acts: Searle (1975): 5 classes of speech acts: Searle (1975) Assertives: committing the speaker to something’s being the case (suggesting, putting forward, swearing, boasting, concluding) Directives: attempts by the speaker to get the addressee to do something (asking, ordering, requesting, inviting, advising, begging) Commissives:Committing the speaker to some future course of action (promising, planning, vowing, betting, opposing). Expressives: expressing the psychological state of the speaker about a state of affairs (thanking, apologizing, welcoming, deploring). Declarations: bringing about a different state of the world via the utterance (I resign; You’re fired)


Grounding: Grounding Dialogue is a collective act performed by speaker and hearer Common ground: set of things mutually believed by both speaker and hearer Need to achieve common ground, so hearer must ground or acknowledge speakers utterance. Clark (1996): Principle of closure. Agents performing an action require evidence, sufficient for current purposes, that they have succeeded in performing it (Interestingly, Clark points out that this idea draws from Norman (1988) work on non-linguistic acts) Need to know whether an action succeeded or failed


Clark and Schaefer: Grounding: Clark and Schaefer: Grounding Continued attention: B continues attending to A Relevant next contribution: B starts in on next relevant contribution Acknowledgement: B nods or says continuer like uh-huh, yeah, assessment (great!) Demonstration: B demonstrates understanding A by paraphrasing or reformulating A’s contribution, or by collaboratively completing A’s utterance Display: B displays verbatim all or part of A’s presentation


A human-human conversation: A human-human conversation


Grounding examples: Grounding examples Display: C: I need to travel in May A: And, what day in May did you want to travel? Acknowledgement C: He wants to fly from Boston A: mm-hmm C: to Baltimore Washington International [Mm-hmm (usually transcribed “uh-huh”) is a backchannel, continuer, or acknowledgement token]


Grounding Examples (2): Grounding Examples (2) Acknowledgement + next relevant contribution And, what day in May did you want to travel? And you’re flying into what city? And what time would you like to leave? The and indicates to the client that agent has successfully understood answer to the last question.


Grounding negative responsesFrom Cohen et al. (2004): Grounding negative responses From Cohen et al. (2004) System: Did you want to review some more of your personal profile? Caller: No. System: Okay, what’s next? System: Did you want to review some more of your personal profile? Caller: No. System: What’s next?


Grounding and Dialogue Systems: Grounding and Dialogue Systems Grounding is not just a tidbit about humans Is key to design of conversational agent Why?


Grounding and Dialogue Systems: Grounding and Dialogue Systems Grounding is not just a tidbit about humans Is key to design of conversational agent Why? HCI researchers find users of speech-based interfaces are confused when system doesn’t give them an explicit acknowledgement signal Stifelman et al. (1993), Yankelovich et al. (1995)


Conversational Structure: Conversational Structure Telephone conversations Stage 1: Enter a conversation Stage 2: Identification Stage 3: Establish joint willingness to converse Stage 4: First topic is raised, usually by caller


Why is this customer confused?: Why is this customer confused? Customer: (rings) Operator: Directory Enquiries, for which town please? Customer: Could you give me the phone number of um: Mrs. um: Smithson? Operator: Yes, which town is this at please? Customer: Huddleston. Operator: Yes. And the name again? Customer: Mrs. Smithson


Conversational Implicature: Conversational Implicature A: And, what day in May did you want to travel? C: OK, uh, I need to be there for a meeting that’s from the 12th to the 15th. Note that client did not answer question. Meaning of client’s sentence: Meeting Start-of-meeting: 12th End-of-meeting: 15th Doesn’t say anything about flying!!!!! What is it that licenses agent to infer that client is mentioning this meeting so as to inform the agent of the travel dates?


Conversational Implicature (2): Conversational Implicature (2) A: … there’s 3 non-stops today. This would still be true if 7 non-stops today. But no, the agent means: 3 and only 3. How can client infer that agent means: only 3


Grice: conversational implicature: Grice: conversational implicature Implicature means a particular class of licensed inferences. Grice (1975) proposed that what enables hearers to draw correct inferences is: Cooperative Principle This is a tacit agreement by speakers and listeners to cooperate in communication


4 Gricean Maxims: 4 Gricean Maxims Relevance: Be relevant Quantity: Do not make your contribution more or less informative than required Quality: try to make your contribution one that is true (don’t say things that are false or for which you lack adequate evidence) Manner: Avoid ambiguity and obscurity; be brief and orderly


Relevance: Relevance A: Is Regina here? B: Her car is outside. Implication: yes Hearer thinks: why would he mention the car? It must be relevant. How could it be relevant? It could since if her car is here she is probably here. Client: I need to be there for a meeting that’s from the 12th to the 15th Hearer thinks: Speaker is following maxims, would only have mentioned meeting if it was relevant. How could meeting be relevant? If client meant me to understand that he had to depart in time for the mtg.


Quantity: Quantity A:How much money do you have on you? B: I have 5 dollars Implication: not 6 dollars Similarly, 3 non stops can’t mean 7 non-stops (hearer thinks: if speaker meant 7 non-stops she would have said 7 non-stops A: Did you do the reading for today’s class? B: I intended to Implication: No B’s answer would be true if B intended to do the reading AND did the reading, but would then violate maxim


Dialogue System Architecture: Dialogue System Architecture


Speech recognition: Speech recognition Or ASR (Automatic Speech Recognition) Speech to words Input: acoustic waveform Output: string of words We’ll introduce the algorithms in week 10 Basic components: a recognizer for phones, small sound units like [k] or [ae]. a pronunciation dictionary like cat = [k ae t] a grammar telling us what words are likely to follow what words A search algorithm to find the best string of words


Natural Language Understanding: Natural Language Understanding Or “NLU” Or “Computational semantics” There are many ways to represent the meaning of sentences For speech dialogue systems, most common is “Frame and slot semantics”.


An example of a frame: An example of a frame Show me morning flights from Boston to SF on Tuesday. SHOW: FLIGHTS: ORIGIN: CITY: Boston DATE: Tuesday TIME: morning DEST: CITY: San Francisco


How to generate this semantics?: How to generate this semantics? Many methods, Simplest: “semantic grammars” We’ll come back to these after we’ve seen parsing. But a quick teaser for those of you who might have already seen parsing: CFG in which the LHS of rules is a semantic category: LIST -> show me | I want | can I see|… DEPARTTIME -> (after|around|before) HOUR | morning | afternoon | evening HOUR -> one|two|three…|twelve (am|pm) FLIGHTS -> (a) flight|flights ORIGIN -> from CITY DESTINATION -> to CITY CITY -> Boston | San Francisco | Denver | Washington


Semantics for a sentence: Semantics for a sentence LIST FLIGHTS ORIGIN Show me flights from Boston DESTINATION DEPARTDATE to San Francisco on Tuesday DEPARTTIME morning


Generation and TTS: Generation and TTS Generation component Chooses concepts to express to user Plans out how to express these concepts in words Assigns any necessary prosody to the words TTS component Takes words and prosodic annotations Synthesizes a waveform


Generation Component: Generation Component Content Planner Decides what content to express to user (ask a question, present an answer, etc) Often merged with dialogue manager Language Generation Chooses syntactic structures and words to express meaning. Simplest method All words in sentence are prespecified! “Template-based generation” Can have variables: What time do you want to leave CITY-ORIG? Will you return to CITY-ORIG from CITY-DEST?


More sophisticated language generation component: More sophisticated language generation component Natural Language Generation This is a field, like Parsing, or Natural Language Understanding, or Speech Synthesis, with its own (small) conference Approach: Dialogue manager builds representation of meaning of utterance to be expressed Passes this to a “generator” Generators have three components Sentence planner Surface realizer Prosody assigner


Architecture of a generator for a dialogue system(after Walker and Rambow 2002): Architecture of a generator for a dialogue system (after Walker and Rambow 2002)


HCI constraints on generation for dialogue: “Coherence”: HCI constraints on generation for dialogue: “Coherence” Discourse markers and pronouns (“Coherence”): (1) Please say the date. … Please say the start time. … Please say the duration… … Please say the subject… (2) First, tell me the date. … Next, I’ll need the time it starts. … Thanks. Now, how long is it supposed to last? … Last of all, I just need a brief description


HCI constraints on generation for dialogue: coherence (II): tapered prompts: HCI constraints on generation for dialogue: coherence (II): tapered prompts Prompts which get incrementally shorter: System: Now, what’s the first company to add to your watch list? Caller: Cisco System: What’s the next company name? (Or, you can say, “Finished”) Caller: IBM System: Tell me the next company name, or say, “Finished.” Caller: Intel System: Next one? Caller: America Online. System: Next? Caller: …


Dialogue Manager: Dialogue Manager Controls the architecture and structure of dialogue Takes input from ASR/NLU components Maintains some sort of state Interfaces with Task Manager Passes output to NLG/TTS modules


Four architectures for dialogue management: Four architectures for dialogue management Finite State Frame-based Information State Markov Decision Processes AI Planning


Finite-State Dialogue Mgmt: Finite-State Dialogue Mgmt Consider a trivial airline travel system Ask the user for a departure city For a destination city For a time Whether the trip is round-trip or not


Finite State Dialogue Manager: Finite State Dialogue Manager


Finite-state dialogue managers: Finite-state dialogue managers System completely controls the conversation with the user. It asks the user a series of questions Ignoring (or misinterpreting) anything the user says that is not a direct answer to the system’s questions


Dialogue Initiative: Dialogue Initiative Systems that control conversation like this are system initiative or single initiative. “Initiative”: who has control of conversation In normal human-human dialogue, initiative shifts back and forth between participants.


System Initiative: System Initiative Systems which completely control the conversation at all times are called system initiative. Advantages: Simple to build User always knows what they can say next System always knows what user can say next Known words: Better performance from ASR Known topic: Better performance from NLU Ok for VERY simple tasks (entering a credit card, or login name and password) Disadvantage: Too limited


User Initiative: User Initiative User directs the system Generally, user asks a single question, system answers System can’t ask questions back, engage in clarification dialogue, confirmation dialogue Used for simple database queries User asks question, system gives answer Web search is user initiative dialogue.


Problems with System Initiative: Problems with System Initiative Real dialogue involves give and take! In travel planning, users might want to say something that is not the direct answer to the question. For example answering more than one question in a sentence: Hi, I’d like to fly from Seattle Tuesday morning I want a flight from Milwaukee to Orlando one way leaving after 5 p.m. on Wednesday.


Single initiative + universals: Single initiative + universals We can give users a little more flexibility by adding universal commands Universals: commands you can say anywhere As if we augmented every state of FSA with these Help Start over Correct This describes many implemented systems But still doesn’t allow user to say what the want to say


Mixed Initiative: Mixed Initiative Conversational initiative can shift between system and user Simplest kind of mixed initiative: use the structure of the frame itself to guide dialogue Slot Question ORIGIN What city are you leaving from? DEST Where are you going? DEPT DATE What day would you like to leave? DEPT TIME What time would you like to leave? AIRLINE What is your preferred airline?


Frames are mixed-initiative: Frames are mixed-initiative User can answer multiple questions at once. System asks questions of user, filling any slots that user specifies When frame is filled, do database query If user answers 3 questions at once, system has to fill slots and not ask these questions again! Anyhow, we avoid the strict constraints on order of the finite-state architecture.


Multiple frames: Multiple frames flights, hotels, rental cars Flight legs: Each flight can have multiple legs, which might need to be discussed separately Presenting the flights (If there are multiple flights meeting users constraints) It has slots like 1ST_FLIGHT or 2ND_FLIGHT so user can ask “how much is the second one” General route information: Which airlines fly from Boston to San Francisco Airfare practices: Do I have to stay over Saturday to get a decent airfare?


Multiple Frames: Multiple Frames Need to be able to switch from frame to frame Based on what user says. Disambiguate which slot of which frame an input is supposed to fill, then switch dialogue control to that frame. Main implementation: production rules Different types of inputs cause different productions to fire Each of which can flexibly fill in different frames Can also switch control to different frame


Defining Mixed Initiative: Defining Mixed Initiative Mixed Initiative could mean User can arbitrarily take or give up initiative in various ways This is really only possible in very complex plan-based dialogue systems No commercial implementations Important research area Something simpler and quite specific which we will define in the next few slides


True Mixed Initiative: True Mixed Initiative


How mixed initiative is usually defined: How mixed initiative is usually defined First we need to define two other factors Open prompts vs. directive prompts Restrictive versus non-restrictive grammar


Open vs. Directive Prompts: Open vs. Directive Prompts Open prompt System gives user very few constraints User can respond how they please: “How may I help you?” “How may I direct your call?” Directive prompt Explicit instructs user how to respond “Say yes if you accept the call; otherwise, say no”


Restrictive vs. Non-restrictive grammars: Restrictive vs. Non-restrictive grammars Restrictive grammar Language model which strongly constrains the ASR system, based on dialogue state Non-restrictive grammar Open language model which is not restricted to a particular dialogue state


Definition of Mixed Initiative: Definition of Mixed Initiative


VoiceXML: VoiceXML Voice eXtensible Markup Language An XML-based dialogue design language Makes use of ASR and TTS Deals well with simple, frame-based mixed initiative dialogue. Most common in commercial world (too limited for research systems) But useful to get a handle on the concepts.


Voice XML: Voice XML Each dialogue is a . (Form is the VoiceXML word for frame) Each generally consists of a sequence of s, with other commands


Sample vxml doc: Sample vxml doc Please choose airline, hotel, or rental car. [airline hotel "rental car"] You have chosen .


VoiceXML interpreter: VoiceXML interpreter Walks through a VXML form in document order Iteratively selecting each item If multiple fields, visit each one in order. Special commands for events


Another vxml doc (1): Another vxml doc (1) I'm sorry, I didn't hear you. - “noinput” means silence exceeds a timeout threshold I'm sorry, I didn't understand that. - “nomatch” means confidence value for utterance is too low - notice “reprompt” command


Another vxml doc (2): Another vxml doc (2) Welcome to the air travel consultant. Which city do you want to leave from? [(san francisco) denver (new york) barcelona] OK, from - “filled” tag is executed by interpreter as soon as field filled by user


Another vxml doc (3): Another vxml doc (3) And which city do you want to go to? [(san francisco) denver (new york) barcelona] OK, to And what date do you want to leave? OK, on


Another vxml doc (4): Another vxml doc (4) OK, I have you are departing from to on send the info to book a flight...


Summary: VoiceXML: Summary: VoiceXML Voice eXtensible Markup Language An XML-based dialogue design language Makes use of ASR and TTS Deals well with simple, frame-based mixed initiative dialogue. Most common in commercial world (too limited for research systems) But useful to get a handle on the concepts.


Summary: Summary The Linguistics of Conversation Basic Conversational Agents ASR NLU Generation Dialogue Manager Dialogue Manager Design Finite State Frame-based Initiative: User, System, Mixed VoiceXML Information-State Dialogue-Act Detection Dialogue-Act Generation