Learning, Adaptation and Personalization in Spoken Dialogue Systems

1. Learning, Adaptation and Personalization in Spoken Dialogue Systems Diane J. Litman* University of Pittsburgh Dept. of Computer Science & LRDC litman@cs.pitt.edu Collaborators: Satinder Singh, Michael Kearns, Marilyn Walker, Charles Isbell, Jessica Howe *this work was done at AT&T Labs – Research, Florham Park, NJ

2. Overview of Research Adaptive and Adaptable Systems • TOOT: automatic optimization within a dialogue via supervised learning [ACL-99, NAACL-00, AAAI-00, NAACL-01, ACL-01] • NJFun: automatic optimizationof default dialogue behavior via reinforcement learning [COLING-00, AAAI-00, JAIR-02] (this talk) • TOOT, CobotDS: personalization within a dialogue via voice control [UM-99, AAAI-02] (CobotDS: this talk) Novel Applications • CobotDS: new types of voice-enabled systems pose many technical challenges (e.g. chat interfaces) [AAAI-02] (this talk)

3. Today’s Talk: Overview of Issues • Builders of spoken dialogue systems face fundamental design choices that strongly influence system performance • Can performance be improved by adapting the system? • Many forms of adaptation • machine learning • user-led • Two Case Studies

4. Outline • I. Reinforcement Learning in Spoken Dialogue Systems • Background • Applying Reinforcement Learning • NJFun: A Reinforcement Learning Spoken Dialogue System • A Controlled User Study • II. CobotDS: A Spoken Dialogue System for Chat • Background: LambdaMOO and Cobot • CobotDS: A Novel Dialogue System • Grammar Personalization, Summarization, and Social Filtering • Observations from a Pilot User Study

5. I. Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun Spoken Dialogue System

6. Spoken Dialogue Systems • Front end: ASR (automatic speech recognition) and TTS (text to speech) • Back end: DB, web, etc. • Middle: dialogue strategy or policy (what action to take at each point) • Dominated by ASR “precision-recall” trade-off ASR DB user spoken dialogue system TTS

7. NJFun Dialogues Unoptimized System Optimized System Task Description:"You have lived in Stanhope for many years but have never managed to visit its historic sites. Today you are feeling virtuous. Use RLDS to find out what you can see this morning.” TTS: Welcome to RLDS. Please say an activity name or say 'list activities' for a list of activities I know about. USER: I would like to visit a historic site in Stanhope this morning. TTS: Did you say you are interested in going to a zoo? USER: No. TTS: I know about amusement parks, aquariums, cruises, historic sites, museums, parks, theaters, wineries, and zoos. Please say an activity name from this list. USER: I would like to visit a historic site. TTS: Did you say you are interested in going to a historic site? USER: Yes. TTS: Please give me more information. USER: ... TTS: Please tell me the location that you are interested in. You can also tell me the time. …. (8 turns omitted) TTS: Thank you for using the system. Please give me feedback by saying 'good', 'so-so', or 'bad'. USER: Bad. TTS: Goodbye and have a nice day! TTS: Welcome to RLDS. How may I help you? USER: I would like to um find about Stanhope historic sites. TTS: Did you say you are interested in Stanhope? USER: Yes. TTS: What time of the day do you want to go? USER: Uh...morning. TTS: Did you say you want to go in the morning? USER: Yes. TTS: I found 2 historic sites in Stanhope that are open in the morning. They are the Lenape Indian Village, and the Waterloo Village. Thank you for using the system. Please give me feedback by saying 'good', 'so-so', or 'bad'. USER: Good. TTS: Goodbye and have a nice day!

8. Initiative Strategy • System initiative vs. user initiative: • Please state your departure city. • How can I help you? • Influences user expectations • ASR grammar must be chosen accordingly • Best choice may differ from state to state! • May depend on user population & task • Early mistakes may be costly (e.g. confirmation) • Delayed reward

9. Typical System Design: Sequential Search • Choose and implement several “reasonable” dialogue policies • Field systems, gather dialogue data • Do statistical analyses • Refield system with “best” dialogue policy • Can only examine a handful of policies

10. Why Reinforcement Learning?(Levin, Pieraccini, Eckert; Walker; Singh, Kearns, Litman, Walker) • Agents can learn to improve performance by interacting with their environment • Thousands of possible dialogue policies, and want to automate the choice of the “optimal” • Can handle many features of spoken dialogue • noisy sensors (ASR output) • stochastic behavior (user population) • delayed rewards, and many possible rewards • multiple plausible actions • However, many practical challenges remain

11. Our Approach • Build initial system that is deliberately exploratory wrt state and action space • Use dialogue data from initial system to build a Markov decision process(MDP) • Use methods of reinforcement learning to compute optimal policy of the MDP, with respect to some reward • Re-field (improved?) system given by the optimal policy

12. State-Based Design • System state: contains information relevant for deciding the next action • Dialogue policy: mapping from current state to system action • Typically hundreds of states, several “reasonable” actions from each state • In practice, need a compressed state

13. Markov Decision Processes • System state s (in S) • System action a in (in A); not all states need have choice • Transition probabilities P(s’|s,a) • Reward functionR(s,a) (stochastic) • Fast algorithms for optimal policy • Our application: P(s’|s,a) models the population of users • Allow choice of actions • Learn best choices • Parallel search in policy space!

14. The Application: NJFun • Dialogue system providing telephone access to a DB of activities in NJ • Want to obtain 3 attributes: • activity type (e.g., wine tasting) • location (e.g., Lambertville) • time (e.g., morning) • Failure to bind: query DB with don’t-care

15. The State Space N.B. Non-state variables record attribute values; state does not condition on previous attributes!

16. Sample Action Choices • Initiative (when T = 0) • user (open prompt and grammar) • mixed (constrained prompt, open grammar) • system (constrained prompt and grammar) • Example • GreetU: “How may I help you?” • GreetS: “Please say an activity name.”

17. Dialogue Policy Class • Specify “reasonable” actions for each state • 42 choice states (binary initiative or confirmation action choices) • no choice for all other states • Small state space (62), large policy space (2^42) • Example choice state • initial state: [1,0,0,0,0,0] • action choices: GreetS, GreetU • Learn optimal action for each choice state

18. The Experiment • Designed 6 specific tasks, each with web survey • 54 training subjects generated 311dialogues • Exploratory training dialogues used to build MDP • Optimal policy for objective (binary) task completion computed and implemented • 21 test subjects performed tasks and web surveys for modified system generated 124 dialogues • Did statistical analyses of performance changes

19. Main Results • Objective task completion (-1 to 3, partial credit): • train mean ~ 1.722, test mean ~ 2.176 • two-sample t-test p-value ~ 0.0289 • Binary task completion: • train mean ~ 51.5%, test mean ~ 63.5% • two-sample t-test p-value ~ 0.05

20. Other Results Subjective measures “move to the middle” rather than improve First graph: It was easy to find the place that I wanted (strongly agree = 5,…, strongly disagree=1) train mean = 3.38, test mean = 3.39, p-value = .98

21. Other Results (continued) Comparison to Human Design • Using exploratory dialogues as a Monte Carlo proxy shows that our learned policy outperforms several standard fixed policies A Sanity Check of the MDP • Correlation between Monte Carlo and MDP

22. Conclusions I • MDPs and RL a natural and promising framework for automated dialogue policy design • First practical empirical test of formalism • Resulted in significant system improvements • Favorable comparison to human-designed strategies • Interesting dialogue results • Care in application: • choice of states and actions • gathering exploratory data • choice of reward to optimize

23. Future Work I • Automate choice of states and actions • Scale to more complex systems • POMDPs due to hidden state • Learn terminal (and non-terminal) reward function • Online rather than batch learning

24. II. CobotDS: A Spoken Dialogue System for Chat

25. A Non-traditional Dialogue System • DS’s commonly provide access to relatively structured and static back-end databases, and users have well-defined, task-oriented goals • CobotDS provides spoken access to a complex social text-chat environment • users participate primarily for entertainment or sense of community • “database” is dynamic and unstructured • imbalance and asynchrony between phone user and chat users • Leads to Differing Research Foci • Personalized Social-Filtering in listen mode • Summarization • Personal grammars

26. CobotDS l a m b d a M O O s e r v e r chat user chat user ASR C O B O T spoken dialogue system phone user TTS chat user chat user

27. LambdaMOO: Whirlwind Tour • Multiuser, text-based virtual world • Chat channel: directed speech and “emotes” • Users create rooms, objects, behaviors • Founded in 1990; > 5K users • History of AI experimentation

28. Sample Text Chat HFh waves to Buster. Buster bows gracefully to HFh. Buster is overwhelmed by all these paper deadlines. Buster begins to slowly tear his hair out, one strand at a time. HFh comforts Buster. HFh [to Buster]: Remember, the mighty oak was once a nut like you. Buster [to HFh]: Right, but his personal growth was assured. Thanks anyway, though. Buster feels better now. Standard verbs and emotes: directed and broadcast speech, hug, wave, bow, nod, eye, poke, zap, grin, laugh, comfort, ...

29. Cobot • Has user status, but known to be a bot • Resides in LambdaMOO Living Room • Primary functionality: • extensive logging and recording • social statistics and queries • emote and chat abilities • reinforcement learning

30. CobotDS • Telephone access to Cobot & LambdaMoo • Dialogue system supports basic emotes, “say” and “listen” commands, info commands,… • CobotDS goals: • Integrate our dialogue system work • Tackle many different research issues • Fielded September 2000

31. Hi, Buster. What do you want to do? Wave. You wave. Who. You who. I am with Cocobot, Betwixt, and HFh. HFh saysHow was the movie?, to you Say HFh. What message would you like to pass? Fantastic. You say Fantastic, to HFh. Betwixt waves to you. Summarize. You summarize. Recently, HFh and Betwixt chatted. Betwixt and Natto bickered. HFh made the most noise. Grammar. Which grammar would you like to use? Personal. The grammar is now set to Buster. Say. What message would you like to pass? I am in Hoboken. You say I am in Hoboken, to Betwixt. Listen. You listen. Betwixt gives you a nod. HFh to Betwixt, Sprewell will go to the rim, but Houston settles for jumpers from the parking lot, & then, I grin to HFh. HFh [to Betwixt]: And thanks to TiVo, I was able to see the game when I got home. Betwixt [to HFh]: The second half was pretty spectacular. Cobot turns to pick up the phone. Cobot begins talking to Buster! Cobot holds up a sign: Buster passes on a wave from the phone. HFh [to Cobot]: phone: How was the movie? Betwixt [to Cobot]: phone: wave Cobot [to HFh]: Fantastic Cobot [to HFh]: That was from Buster. Cobot holds up a sign: Buster says, 'I am in Hoboken' from the phone. Betwixt [to Cobot]: phone: nod Cobot holds up a sign: Buster passes on a listen from the phone. HFh [to Betwixt]: Sprewell will go to the rim, but Houston settles for jumpers from the parking lot. Cobot grins at HFh. HFh [to Betwixt]: With Camby's rebounding they have a chance to at least win the East. Betwixt [to HFh]: Good point.

32. Listen withSocial Filtering Listen puts phone user in “radio” mode Personalized social filtering: delete least-interacted first Size of filtering tuned to minimize lag Play-by-play mode Summarization Summary of last n minutes of activity Use social statistics to determine most active users Entry and exit of users “friendly” vs. “nasty” interaction Batch mode Dealing with the flood of chat

33. Personalization of Grammars • Phone user could change grammar used through command grammar and then engaging in subdialogue • Two built-in grammars: • smalltalk – 228 hand-constructed phrases provide basic conversation, e.g., “yes”, “no”, “fantastic”, “terrible”, “I’m at home”, etc. • Cliché – 2950 common English sayings, e.g., “taking coal to Pittsburgh ”, etc. • One personal grammar • Comprised of list of phrases provided by each phone user

34. CobotDS Observations • Observations from most frequent 18 non-authors who made calls • Calls for some users averaged 20 minutes, occasionally last for 45 minutes or more • Phone users vary use of speech/verbs • Interaction included a variety of LambdaMOO users • Average lag ten seconds, filtering rare, socially filtered text allowed 70% of unfiltered length • Personalized grammars used early and often, evolved to deal with ASR errors • Switching in regimes from radio/listen/interaction

35. Conclusions II • Users • Adapted personal grammar to deal with ASR errors – “I can’t answer that question using my limited vocab.”, “I didn’t mean to say that”, etc. • added sentences with acoustically distinct keywords to increase recognition • wanted to update personal grammar instantaneously • wanted explicit confirmation of utterance in “say” • wanted to selectively listen to the activity of some chat users Some lessons for “social” (and multi-modal) spoken dialogue systems

36. Future Work II • Investigate similar issues in the context of collaborative learning and tutoring systems

37. Conclusions • Adaptation via Learning and Personalization can improve dialogue system performance • NJFun:Given a small number of exploratory dialogues, reinforcement learning can be used in a working system to choose a more optimal policy within a large search space • CobotDS:Personalization can be useful and entertaining in a social environment; such applications pose interesting and different research challenges

38. Backup Slides

39. Sample Dialogue S1: Welcome to NJFun. How may I help you? U1: I’d like to find um winetasting in Lambertville in the morning. (ASR output: I’d like to find out wineries the in the Lambertville in the morning.) S2: Did you say you are interested in Lambertville? U2: Yes S3: Did you say you want to go in the morning? U3: Yes. S4. I found a winery near Lambertville that is open in the morning. It is […] Please give me feedback by saying “good”, “so-so” or “bad”. U4: Good

40. Confirmation Policy • High ASR confidence: accept ASR match and move on • Moderate ASR confidence: confirm • Low ASR confidence: re-ask • How to set confidence thresholds? • Early mistakes can be costly later, but excessive confirmation is annoying

41. Computing the Optimal • Given parameters P(s’|s,a), R(s,a), can efficiently compute policy maximizing expected return • Typically compute the expected cumulative reward (or Q-value) Q(s,a), using value iteration • Optimal policy selects the action with the maximum Q-value at each dialogue state

42. Potential Benefits • A principled and general framework for automated dialogue policy synthesis • learn the optimal action to take in each state • Compares all policies simultaneously • data efficient because actions are evaluated as a function of state • traditional methods evaluate entire policies • Potential for “lifelong learning” systems, adapting to changing user populations

43. Actions: initiative: open or closed prompt? open or closed grammar? confirmation: confirm, re-ask, move on? binary choices only “reasonable” states conservative actions State features: ASR scores barge-in counts number of tries time-outs ASR-centric Actions and State 42 states with binary action choice; no function approximation

44. Sample Confirmation Choices • Confirmation (when V = 1) • confirm • no confirm • Example • Conf3: “Did you say want to go in the <time>?” • NoConf3: “”

45. Some System Details • Uses AT&T’s WATSON ASR and TTS platform, DMD dialogue manager • Natural language web version used to build multiple ASR language models • Initial statistics used to tune bins for confidence values, history bit (informative state encoding)

46. Main Results On all dialogues: • Objective task completion (-1 to 3, partial credit): • train mean ~ 1.722, test mean ~ 2.176 • two-sample t-test p-value ~ 0.0289 • Binary task completion: • train mean ~ 51.5%, test mean ~ 63.5% • two-sample t-test p-value ~ 0.05 On “expert” dialogues 3-6: • Binary task completion • - train mean ~ 45.6%, test mean ~ 68.2% • - two-sample t-test p-value ~ 0.001

47. Comparison to Human Design • Fielded comparison infeasible, but exploratory dialogues provide a Monte Carlo proxy of “consistent trajectories” • Testpolicy: Average binary completion reward = 0.67 (based on 12 trajectories) • Outperforms several standard fixed policies • SysNoConfirm: -0.08 (11) • SysConfirm: -0.6 (5) • UserNoConfirm: -0.2 (15) • Mixed: -0.077 (13) • User Confirm: 0.2727 (11), no difference

48. A Sanity Check of the MDP • Generate many random policies • Compare value according to MDP and value based on consistent exploratory trajectories • MDP evaluation of policy: ideally perfectly accurate (infinite Monte Carlo sampling), linear fit with slope 1, intercept 0 • Correlation between Monte Carlo and MDP: • 1000 policies, > 0 trajs: cor. 0.31, slope 0.953, int. 0.067, p < 0.001 • 868 policies, > 5 trajs: cor. 0.39, slope 1.058, int. 0.087, p < 0.001

49. Related Work • Biermann and Long (1996) • Levin, Pieraccini, and Eckert (1997) • Walker, Fromer, and Narayanan (1998) • Singh, Kearns, Litman, and Walker (1999) • Scheffler and Young (2000) • Beck, Woolf, and Beal (2000) • Roy, Pineau, and Thrun (2000)

50. Popularity social statistics emotes chat Cumulative Interactions: Population