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Error Awareness and Recovery in Task-Oriented Spoken Dialogue Systems Thesis Proposal Dan Bohus Carnegie Mellon University, January 2004 Thesis Committee Alex Rudnicky (Chair) Roni Rosenfeld Jeff Schneider Eric Horvitz (Microsoft Research)
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2 Problem Spans most domains and interaction types Has a significant impact on performance Lack of robustness when faced with understanding errors
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3 An example S: What city are you leaving from? U: Urbana Champaign [OKAY IN THAT SAME PAY] S: Sorry Im not sure I understood what you said. What city are you leaving from ? U: Urbana Champaign [FOR MINUTE SINCE HEY] S: Im still having trouble understanding you. To go on, I need you to answer the following question. What city are you leaving from? U: Chicago [CHICAGO] S: traveling from Chicago. Where would you like to go? U: Huntsville [SEOUL] S: traveling to Seoul. What day did you need to travel? U: no no Im traveling to Birmingham [THE TRAVELING TO BERLIN P_M] S: traveling in the afternoon. Okay what day would you be departing chicago U: /uh/ the tenth of august [AT THE TENTH OF AUGUST] S: a flight tomorrow… I have a Korean Airlines flight departing Chicago at 1:40pm, arrives Seoul at 5pm the next day. Do you want to take that? U: my destination is Birmingham [FLIGHT DESTINATION MR WEEKEND] S: traveling on Saturday, August 12 th … I have a flight departing Chicago at 1:40pm arrives Seoul at ………
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4 Some Statistics … Corrections [Krahmer, Swerts, Litman, Levow] 30% of utterances correct system mistakes 2-3 times more likely to be misrecognized Semantic error rates: ~25-35% SpeechActs [SRI] 25% CU Communicator [CU] 27% Jupiter [MIT] 28% CMU Communicator [CMU] 32% How May I Help You? [AT&T] 36%
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5 Significant Impact on Interaction CMU Communicator 40% 26% Contain understanding errors Failed Multi-site Communicator Corpus [Shin et al] 37% Failed sessions 33% 63%
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6 Outline Problem Approach Infrastructure Research Program Summary & Timeline problem : approach : infrastructure : indicators : strategies : decision process : summary
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7 Increasing Robustness … Increase the accuracy of speech recognition Assume recognition is unreliable, and create the mechanisms for acting robustly at the dialogue management level problem : approach : infrastructure : indicators : strategies : decision process : summary
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8 Snapshot of Existing Work: Slide 1 Theoretical models of grounding Contribution Model [Clark], Grounding Acts [Traum] Practice: heuristic rules Misunderstandings Threshold(s) on confidence scores Non-understandings problem : approach : infrastructure : indicators : strategies : decision process : summary Analytical/Descriptive, not decision oriented Ad-hoc, lack generality, not easy to extend
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9 Snapshot of Existing Work: Slide 2 Conversation as Action under Uncertainty [Paek and Horvitz] Belief networks to model uncertainties Decisions based on expected utility, VOI-analysis Reinforcement learning for dialogue control policies [Singh, Kearns, Litman, Walker, Levin, Pieraccini, Young, Scheffler, etc] Formulate dialogue control as an MDP Learn optimal control policy from data Do not scale up to complex, real-world tasks problem : approach : infrastructure : indicators : strategies : decision process : summary
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10 Develop a task-independent, adaptive and scalable framework for error recovery in task-oriented spoken dialogue systems Thesis Statement Decision making under uncertainty Approach: problem : approach : infrastructure : indicators : strategies : decision process : summary
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11 1.Error awareness 2.Error recovery strategies 3.Error handling decision process Three components Develop indicators that … Assess reliability of information Assess how well the dialogue is advancing Develop and investigate an extended set of conversational error handling strategies Develop a scalable reinforcement-learning based architecture for making error handling decisions problem : approach : infrastructure : indicators : strategies : decision process : summary 0.Infrastructure problem : approach : infrastructure : indicators : strategies : decision process : summary
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12 Infrastructure RavenClaw Modern dialog management framework for complex, task-oriented domains RavenClaw spoken dialogue systems Test-bed for evaluation problem : approach : infrastructure : indicators : strategies : decision process : summary Completed
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13 RavenClaw Dialogue Task (Specification) Domain-Independent Dialogue Engine RoomLine Login Welcome AskRegisteredAskName GreetUser GetQuery DateTimeLocationProperties NetworkProjectorWhiteboard GetResultsDiscussResults user_nameregistered query results RoomLine Login AskRegistered Dialogue Stack registered: [No]-> false, [Yes] -> true Expectation Agenda Error Handling Decision Process Strategies Error Indicators ExplicitConfirm problem : approach : infrastructure : indicators : strategies : decision process : summary
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14 RavenClaw-based Systems problem : approach : infrastructure : indicators : strategies : decision process : summary SystemDomain RoomLineInformation Access CMU Lets Go! Bus Information System Information Access LARRI [Symphony] Guidance through procedures Intelligent Procedure Assistant [NASA Ames] Guidance through procedures TeamTalk [11-754] Command-and-control Eureka [11-743] Web-access
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15 0.Infrastructure 1.Error awareness 2.Error recovery strategies 3.Error handling decision process Research Plan Develop indicators that … Assess reliability of information Assess how well the dialogue is advancing Develop and investigate an extended set of conversational error handling strategies Develop a scalable reinforcement-learning based architecture for making error handling decisions problem : approach : infrastructure : indicators : strategies : decision process : summary
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16 Existing Work Confidence Annotation Traditionally focused on speech recognizer [Bansal, Chase, Cox, and others] Recently, multiple sources of knowledge [San-Segundo, Walker, Bosch, Bohus, and others] Recognition, parsing, dialogue management Detect misunderstandings: ~ 80-90% accuracy Correction and Aware Site Detection [Swerts, Litman, Levow and others] Multiple sources of knowledge Detect corrections: ~ 80-90% accuracy problem : approach : infrastructure : indicators : strategies : decision process : summary
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17 S: Where are you flying from? U: [CityName={Aspen/0.6; Austin/0.2}] S: Did you say you wanted to fly out of Aspen? U: [No/0.6] [CityName={Boston/0.8}] Proposed: Belief Updating Continuously assess beliefs in light of initial confidence and subsequent events [CityName={Aspen/?; Austin/?; Boston/?}] An example: problem : approach : infrastructure : indicators : strategies : decision process : summary initial belief + system action + user response updated belief
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18 contents Belief Updating: Approach Model the update in a dynamic belief network User concept User response tt + 1 problem : approach : infrastructure : indicators : strategies : decision process : summary confidence correction 1 st Hyp2 nd Hyp3 rd Hyp Confidence YesNo Positive Markers Negative Markers Utterance Length User concept System action
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19 0.Infrastructure 1.Error awareness 2.Error recovery strategies 3.Error handling decision process Research Plan Develop indicators that … Assess reliability of information Assess how well the dialogue is advancing Develop and investigate an extended set of conversational error handling strategies Develop a scalable reinforcement-learning based architecture for making error handling decisions problem : approach : infrastructure : indicators : strategies : decision process : summary
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20 Is the Dialogue Advancing Normally? Locally, turn-level: Non-understanding indicators Non-understanding flag directly available Develop additional indicators Recognition, Understanding, Interpretation Globally, discourse-level: Dialogue-on-track indicators Counts, averages of non-understanding indicators Rate of dialogue advance problem : approach : infrastructure : indicators : strategies : decision process : summary
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21 0.Infrastructure 1.Error awareness 2.Error recovery strategies 3.Error handling decision process Research Plan Develop indicators that … Assess reliability of information Assess how well the dialogue is advancing Develop and investigate an extended set of conversational error handling strategies Develop a scalable reinforcement-learning based architecture for making error handling decisions problem : approach : infrastructure : indicators : strategies : decision process : summary
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22 Error Recovery Strategies Identify Identify and define an extended set of error handling strategies Implement Construct task-decoupled implementations of a large number of strategies Evaluate Evaluate performance and bring further refinements problem : approach : infrastructure : indicators : strategies : decision process : summary
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23 List of Error Recovery Strategies Help Where are we? Start over Scratch concept value Go back Channel establishment Suspend/Resume Repeat Summarize Quit Restart subtask plan Select alternative plan Start over Terminate session / Direct to operator Local problems (non-understandings) Global problems (compounded, discourse-level problems) Switch input modality SNR repair Ask repeat turn Notify non-understanding Explicit confirm turn Targeted help WH-reformulation Keep-a-word reformulation Generic help You can say Ask rephrase turn problem : approach : infrastructure : indicators : strategies : decision process : summary User InitiatedSystem Initiated Explicit confirmation Implicit confirmation Disambiguation Ask repeat concept Reject concept Ensure that the system has reliable information (misunderstandings) Ensure that the dialogue on track
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24 List of Error Recovery Strategies Help Where are we? Start over Scratch concept value Go back Channel establishment Suspend/Resume Repeat Summarize Quit Restart subtask plan Select alternative plan Start over Terminate session / Direct to operator Local problems (non-understandings) Global problems (compounded, discourse-level problems) Switch input modality SNR repair Ask repeat turn Notify non-understanding Explicit confirm turn Targeted help WH-reformulation Keep-a-word reformulation Generic help You can say Ask rephrase turn problem : approach : infrastructure : indicators : strategies : decision process : summary User InitiatedSystem Initiated Explicit confirmation Implicit confirmation Disambiguation Ask repeat concept Reject concept Ensure that the system has reliable information (misunderstandings) Ensure that the dialogue on track
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25 Error Recovery Strategies: Evaluation Reusability Deploy in different spoken dialogue systems Efficiency of non-understanding strategies Simple metric: Is the next utterance understood? Efficiency depends on decision process Construct upper and lower bounds for efficiency Lower bound: decision process which chooses uniformly Upper bound: human performs decision process (WOZ) problem : approach : infrastructure : indicators : strategies : decision process : summary
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26 0.Infrastructure 1.Error awareness 2.Error recovery strategies 3.Error handling decision process Research Plan Develop indicators that … Assess reliability of information Assess how well the dialogue is advancing Develop and investigate an extended set of conversational error handling strategies Develop a scalable reinforcement-learning based architecture for making error handling decisions problem : approach : infrastructure : indicators : strategies : decision process : summary
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27 Dialogue control ~ Markov Decision Process States Actions Rewards Previous work: successes in small domains NJFun [Singh, Kearns, Litman, Walker et al] Problems Approach does not scale Once learned, policies are not reusable Previous Reinforcement Learning Work problem : approach : infrastructure : indicators : strategies : decision process : summary S1S1 S2S2 S3S3 A R
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28 Proposed Approach Overcome previous shortcomings: Focus learning only on error handling Reduces the size of the learning problem Favors reusability of learned policies Lessens the system development effort Use a divide-and-conquer approach Leverage independences in dialogue problem : approach : infrastructure : indicators : strategies : decision process : summary
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29 Decision Process Architecture RoomLine Login Welcome AskRegisteredAskName GreetUser user_nameregistered Gating Mechanism Concept-MDP Topic-MDP Small-size models Parameters can be tied across models Accommodate dynamic task generation Favors reusability of policies Initial policies can be easily handcrafted problem : approach : infrastructure : indicators : strategies : decision process : summary No Action Explicit Confirm No Action Explicit Confirmation Independence assumption
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30 Reward Structure & Learning Gating Mechanism MDP Action Global, post-gate rewards Reward Gating Mechanism MDP Action Local rewards Reward Rewards based on any dialogue performance metric Atypical, multi-agent reinforcement learning setting Multiple, standard RL problems Model-based approaches problem : approach : infrastructure : indicators : strategies : decision process : summary
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31 Evaluation Performance Compare learned policies with initial heuristic policies Metrics Task completion Efficiency Number and lengths of error segments User satisfaction Scalability Deploy in a system operating with a sizable task Theoretical analysis problem : approach : infrastructure : indicators : strategies : decision process : summary
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32 Outline Problem Approach Infrastructure Research Program Summary & Timeline problem : approach : infrastructure : indicators : strategies : decision process : summary
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33 Goal: develop a task-independent, adaptive and scalable framework for error recovery in task-oriented spoken dialogue systems Modern dialogue management framework Belief updating framework Investigation of an extended set of error handling strategies Scalable data-driven approach for learning error handling policies Summary of Anticipated Contributions problem : approach : infrastructure : indicators : strategies : decision process : summary
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34 Timeline proposal milestone 1 milestone 2 milestone 3 defense end of year 4 end of year 5 now 5.5 years Data collection for belief updating and WOZ study Develop and evaluate the belief updating models Implement dialogue-on-track indicators Misunderstanding and non-understanding strategies Investigate theoretical aspects of proposed reinforcement learning model Evaluate non-understanding strategies; develop remaining strategies Error handling decision process: reinforcement learning experiments Data collection for RL training Data collection for RL evaluation data indicatorsstrategiesdecisions Contingency data collection efforts Additional experiments: extensions or contingency work problem : approach : infrastructure : indicators : strategies : decision process : summary February 2004 September 2004 September 2005 January 2005 December 2005
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35 Thank You! Questions & Comments
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36 Additional Slides
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38 Understanding Process Errors in spoken dialogue systems Recognition System acquires information System does not acquire information Non-understanding System acquires correct information System acquires incorrect information Misunderstanding OK Parsing Contextual Interpretation Non-understanding indicators/ Turn-level strategies Belief Updating/ Concept-level strategies
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39 Structure of Individual MDPs HC ExplConf ImplConf NoAct LC ExplConf ImplConf NoAct MC ExplConf ImplConf NoAct 0 Concept MDPs State-space: belief indicators Action-space: concept scoped system actions Topic MDPs State-space: non-understanding, dialogue-on-track indicators Action-space: non-understanding actions, topic-level actions
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40 Gating Mechanism Heuristic derived from domain-independent dialogue principles Give priority to entities closer to the conversational focus Give priority to topics over concept
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41 Task-independence / Reusability Dialogue Task (Specification) Domain-Independent Dialogue Engine RoomLine Login Welcome AskRegisteredAskName GreetUser GetQuery DateTimeLocationProperties NetworkProjectorWhiteboard GetResultsDiscussResults user_nameregistered query results RoomLine Login AskRegistered Dialogue Stack registered: [No]-> false, [Yes] -> true Expectation Agenda Error Handling Decision Process Strategies Error Indicators ExplicitConfirm problem : approach : infrastructure : indicators : strategies : decision process : summary Argument : architecure Proof: deployment across multiple RavenClaw systems
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42 Adaptable problem : approach : infrastructure : indicators : strategies : decision process : summary Argument: reinforcement learning approach Proof: longer term evaluation of adaptability (extension work item) RoomLine Login Welcome AskRegisteredAskName GreetUser user_nameregistered Gating Mechanism Concept-MDP Topic-MDP No Action Explicit Confirm No Action Explicit Confirmation
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43 Scalable problem : approach : infrastructure : indicators : strategies : decision process : summary Argument: architecture Proof: deployment and experiments with systems with large tasks RoomLine Login Welcome AskRegisteredAskName GreetUser user_nameregistered Gating Mechanism Concept-MDP Topic-MDP No Action Explicit Confirm No Action Explicit Confirmation
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44 Scalability of Reinforcement Learning NJFun 3 concepts, 7 state variables, 62 states Learned a policy from 311 dialogues Consider 12 concepts (RoomLine/20, CMU Lets Go!/27) 242 states State-space: grows 4 times # Parameters: grows 16 times
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45 Extension Work Items Portability of confidence annotation and belief updating schemes Use domain-independent features Train in one domain, test in another Use of unlabeled data Self-training and co-training to improve performance in a new domain Evaluation of adaptability/reusability Reusability: migrate policies from one domain to another Adaptability: monitor model changes and system behavior throughout a period of extended use
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46 Study by [Shin et al] Labeled error segments in 141 dialogs from multiple Communicator systems 1.66 error segments / session 22% of the error segments never get back on track 37% of sessions have breakdowns in interaction
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47 Errors Indicators, Strategies & Decision Process RoomLine Login Welcome AskRegisteredAskName GreetUser user_nameregistered Gating Mechanism Concept-MDP Topic-MDP No Action Explicit Confirm No Action Explicit Confirmation Error indicators Strategies Decision Process
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48 An example S: Where do you want to go to? U: I wanna go to Grand Rapids Michigan on December first [I WANNA GO TO GRAND RAPIDS MICHIGAN FOLLOWING DECEMBER BOSTON] S: traveling to Boston… departing Pittsburgh on what date? U: traveling to Grand Rapids Michigan [TRAVELING TO GRAND RAPIDS MICHIGAN] S: traveling to Grand Rapids… on what date would you like to travel? U: December first [DECEMBER FOR SHIT] S: sorry, Im not sure I understood what you said. What day did you need to travel? U: Friday December first [FRIDAY DECEMBER FOURTH] S: a flight on Monday, December fourth… and what time would you like to leave Pittsburgh Monday, December fourth U: traveling on Friday December first [COMING FRIDAY DECEMBER] S: the date you gave was ambiguous. Did you mean November twenty-third or November twenty fourth? U: traveling on Friday December first [TRAVELING ON FRIDAY DECEMBER] S: a flight on Friday, November twenty-fourth… and departing Pittsburgh at what time? U: traveling the following week on Friday [IM TRAVELING FOLLOWING WEEK ON FRIDAY]
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