Diane Litman University of Pittsburgh

1. Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun Spoken Dialogue System Diane Litman University of Pittsburgh (work performed at AT&T Labs – Research with S. Singh, M. Kearns, and M. Walker) http://www.cs.pitt.edu/~litman

2. Research Motivations • Builders of real-time spoken dialogue systems face fundamental design choices that strongly influence system performance • automatic optimization of default dialogue behavior via reinforcement learning? [COLING-00, AAAI-00, JAIR-02] (and this talk) • personalization via supervised learning and voice control [ACL-99, UM-99, NAACL-00, AAAI-00, NAACL-01, ACL-01] • New types of voice-enabled systems pose many interesting technical challenges (e.g. chat interfaces )

3. Outline • Spoken dialogue systems • Reinforcement learning for dialogue • The NJFun application • Empirical evaluation • Discussion

4. Spoken Dialogue Systems • Provide voice access to back end via telephone or microphone • Front end: ASR (automatic speech recognition) and TTS (text to speech) • Back end: DB, web, etc. • Middle: dialogue policy(what action to take at each point in a dialogue) ASR DB user dialogue manager TTS

5. RLDS 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!

6. Automatic Speech Recognition (ASR) • Inputs: audio file; grammar/language model; acoustic model • Outputs: utterance matched from grammar, or no match; confidence score • Performance tradeoff: • “small” grammar --> high accuracy on constrained utterances, lots of no-matches • “large” grammar --> match more utterances, but with lower confidence

7. Some Issues in Dialogue Policy Design • Initiative policy • Confirmation policy • Criteria to be optimized

8. Initiative Policy • System initiative vs. user initiative: • “Please state your departure city.” • “How can I help you?” • Influences expectations • ASR grammar must be chosen accordingly • Best choice may differ from state to state • May depend on user population & task

9. 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

10. Criteria to be Optimized • Task completion • Sales revenues • User satisfaction • ASR performance • Number of turns

11. 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

12. Why Reinforcement Learning? • 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

13. 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 (here, dialogue policy) of the MDP • Refield (improved?) system given by the optimal policy • Empirically evaluate

14. State-Based Design • System state: contains information relevant for deciding the next action • info attributes perceived so far • individual and average ASR confidences • data on particular user • etc. • In practice, need a compressed state • Dialogue policy: mapping from each state in the state space to a system action

15. Markov Decision Processes • System state s (in S) • System action a in (in A) • Transition probabilities P(s’|s,a) • Reward functionR(s,a) (stochastic) • Our application: P(s’|s,a) models the population of users

16. SDSs as MDPs Initial system utterance Initial user utterance Actions have prob. outcomes + system logs a e a e a e ... 1 1 2 2 3 3 estimate transition probabilities... P(next state | current state & action) ...and rewards... R(current state, action) ...from set ofexploratorydialogues (random action choice) Violations of Markov property! Will this work?

17. 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

18. 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

19. 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 an attribute: query DB with don’t-care

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

21. 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.”

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

23. 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

24. 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)

25. The Experiment • Designed 6 specific tasks, each with web survey • Split 75 internal subjects into training and test, controlling for M/F, native/non-native, experienced/inexperienced • 54 training subjects generated 311 dialogues • Training dialogues used to build MDP • Optimal policy for BINARY TASK COMPLETION computed and implemented • 21 test subjects (for modified system) generated 124 dialogues • Did statistical analyses of performance changes

26. Reward Function • Binary task completion (objective measure): • 1 for 3 correct bindings, else -1 • Task completion (allows partial credit): • -1 for an incorrect attribute binding • 0,1,2,3 correct attribute bindings • Other evaluation measures: ASR performance (objective), and phone feedback, perceived completion, future use, perceived understanding, user understanding, ease of use (all subjective) • Optimized for binary task completion, but predicted improvements in other measures

27. Main Results • Task completion (-1 to 3): • train mean = 1.72 • test mean = 2.18 • p-value < 0.03 • Binary task completion: • train mean = 51.5 % • test mean = 63.5 % • p-value < 0.06

28. Other Results • ASR performance (0-3): • train mean = 2.48 • test mean = 2.67 • p-value < 0.04 • Binary task completion for experts (dialogues 3-6): • train mean = 45.6% • test mean = 68.2 % • p-value < 0.01

29. Subjective Measures 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

30. 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

31. 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

32. Future Work • 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

33. 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)

34. Conclusions • MDPs and RL are a promising framework for automated dialogue policy design • Practical methodology for system-building • given a relatively small number of exploratory dialogues, learn the optimal policy within a large policy search space • Our application: first empirical test of formalism • Resulted in measurable and significant system improvements, as well as interesting linguistic results