1. 1 Chapter 19: Dialogue and Conversational Agents Nadia Hamrouni and Ahmed Abbasi 12/5/2006

2. 2 Applications of Dialogue Agents Conversational agents useful for: –Booking airline flights –Answering questions –Electronic Customer Relationship Management (e-CRM) systems

3. 3 Characteristics of Dialogue –Turns and Utterances Dialogue is characterized by turn-taking Overlapping is small (less than 5%). Speaker transitions occur at utterance boundaries –Boundaries based on cue words (e.g., “well, and, so”) –Grounding Speaker and hearer must establish common ground (the set of things mutually believed) Done via: –attention, acknowledgement, contribution, demonstration, and display –Conversational Implicature Utterance interpretation relies on more than sentence meaning. Requires drawing of inferences. –A - “What day in May did you want to travel?” –C - “I need to be there for a meeting from the 12 th to the 15 th.”

4. 4 Dialogue Acts Speech Acts: –Locutionary act –Illocutionary act –Perlocutionary act Dialogue Acts / Conversational Moves –Include various types of conversational functions. Dialogue Act Markup in Several Layers (DAMSL) architecture – Dialogue act tagging scheme –Hierarchical tag set –Codes levels of dialogue information e.g. forward looking function, backward looking function. –focused on task-oriented dialogue

5. 5 Automatic Interpretation of Dialogue Acts Two types of models: –Plan Inference Models –Cue-Based Models

6. 6 Plan Inference Rules Rule based techniques consisting of manually crafted rule sets. Rules designed for “AI Planning” –How hearer will handle speaker requests –Also called action schema Includes constraints, preconditions, effects, and body. Based on BDI models (Allen, 1995) –Belief, Desire, Intention Belief modeled using KNOWs and KNOWIFs Desire modeled using WANTs

7. 7 Plan Inference Rules Can you give me a list of flights from Atlanta? Step 1: Decompose request: S.REQUESTS(S,H,InformIf(H,S,CanDo(H,Give(H,S,LIST))))) Step 2: B(H,W(S,InformIf(H, S,CanDo(H,Give(H,S,LIST))))) Step 3: B(H,W(S,KnowIf(H,S,CanDo(H,Give(H,S,LIST))))) Step 4: B(H,W(S,CanDo(H,Give(H,S,LIST)))) Step 5: B(H,W(S,Give(H,S,LIST))) Step 6: REQUEST(H,S,Give(H,S,LIST))

8. 8 Plan Inference Rules Advantages –Extremely powerful –Combines rich knowledge structures and planning techniques Can capture direct and indirect uses of dialogue Disadvantages –Time consuming and labor intensive –Accounting for all possible reasoning makes this approach AI-Complete.

9. 9 Cue-based Interpretation Supervised machine learning techniques Trained on hand-labeled dialogue corpora –Use cues (linguistic features) for identifying dialog types. –Word features: “please” “would you”  REQUEST –Conversational Structure “yeah” after proposal  AGREEMENT

10. 10 Cue-based Interpretation Decision Tree Models –Shriberg et al. (1998) –Used Decision tree models trained to differentiate statements, yes-no questions, wh-questions, and declarative questions. HMM Models –Woszczyna and Waibel (1994) –Build markov models of speech act probabilities. Similar to n-gram models, use Bayes’ Rule D* = argmax P(D|C) D

11. 11 Cue-based Interpretation Advantages –Data driven approach less time consuming. –Use of machine learning with availability of large corpora and modern computing power make such methods highly efficient. Disadvantages –Not as sophisticated and accurate as the plan inference approach.

12. 12 Evolution of Conversation Agents ELIZA –Weizenbaum (1966) –Simple dialogue manager –Match previous sentence to set of conditions PARRY –Colby et al. (1971) –Paranoid agent with emotional states and delusions Emotions included anger, fear, etc. BDI Model –Cohen and Perrault (1979) –Still prevalent due to high accuracy Machine Learning –1990s – Present

13. 13 Multimodal Agents REA –(Bickmore & Cassell, 2004) –Developed at the MIT Media Lab –Embodied Agent “Human” agents considered more trustworthy (Kiesler & Sproull, 1997). –Designed to be a real estate agent –Rule based system

14. 14 Multimodal Agents COMIC –(Foster & Oberlander, 2004) –Animated Embodied Agents –Use machine learning algorithms to build agent models –Models trained on corpus of video recordings of conversations. –Models consider speech, facial expressions, body language, and discussion context.

15. 15 References Allen, J. (1995). Natural Language Understanding. Benjamin Cummings, Menlo Park, CA. Bickmore T. & Cassell, J. (2004). Social Dialogue with Embodied Conversational Agents. In J. van Kuppevelt, L. Dybkjaer & N. Bernsen (Eds.), Natural, Intelligent and Effective Interaction with Multimodal Dialogue Systems. New York: Kluwer Academic. Colby, K. M., Weber, S., & Hilf, F. D. (1971). Artificial Paranoia. Artificial Intelligence, 2(1), 1-25. Foster, M. E. & Oberlander, J. (2006). Data-driven Generation of Emphatic Facial Displays. Proceedings of the EACL (2006). Kiesler, S., & Sproull, L. (1997). 'Social' Human-Computer Interaction. In B. Friedman (Ed.), Human Values and the Design of Computer Technology (pp. 191-199). Stanford, CA: CSLI Publications. Shriberg, E., Bates, R., et al. (1989). Can Prosody Aid the Automatic Classification of Dialog Acts in Conversational Speech? Language and Speech, 41(3-4), 439-487. Weizenbaum, J. (1966). ELIZA – A Computer Program for the Study of Natural Language Communication Between Man and Machine. Communication of the ACM, 9(1), 36-45. Woszczyna, M. and Waibel, A. (1994). Inferring Linguistic Structure in Spoken Language. ICSLP-94, 847-850.