Dialog Management Intelligent Robot Lecture Note 1 Dialog

Dialog Management Intelligent Robot Lecture Note 1

Dialog Management Dialog System & Architectures Intelligent Robot Lecture Note 2

Dialog Management Dialogue System • A system to provide interface between the user and a computerbased application • Interact on turn-by-turn basis • Dialogue manager ► ► Control the flow of the dialogue Main flow ◦ information gathering from user ◦ communicating with external application ◦ communicating information back to the user ► Three types of dialogue system ◦ finite state- (or graph-) based ◦ frame-based ◦ agent-based Intelligent Robot Lecture Note 3

Dialog Management Dialog System Architecture • Typical dialog system has following components ► User Interface ◦ Input: Speech Recognition, keyboard , Pen-gesture recognition. . ◦ Output: Display, Sound, Vibration. . ► Context Interpretation ◦ Natural language understanding (NLU) ◦ Reference resolution ◦ Anaphora resolution ► Dialog Management ◦ History management ◦ Discourse management • Many dialog system architectures are introduced. ► ► ► DARPA Communicator GALAXY Communicator etc. Intelligent Robot Lecture Note 4

Dialog Management Dialog System Architecture • The DARPA Communicator program was designed to support the creation of speech-enabled interfaces that scale gracefully across modalities, from speech-only to interfaces that include graphics, maps, pointing and gesture. MIT CMU AT&T DARPA CU SRI Bell Lab BBN Intelligent Robot Lecture Note 5

Dialog Management Galaxy Communicator • The Galaxy Communicator software infrastructure is a distributed, message-based, hub-and-spoke infrastructure optimized for constructing spoken dialogue systems. • An open source architecture for constructing dialogue systems • History ► ► ► MIT Galaxy system Developed and maintained by MITRE Corporation Current version is 4. 0 Intelligent Robot Lecture Note 6

Dialog Management Galaxy Communicator • The architecture Intelligent Robot Lecture Note 7

Dialog Management Galaxy Communicator • Message Passing Protocol Intelligent Robot Lecture Note 8

Dialog Management CU Communicator • Dialogue management in CU Communicator ► Event-driven approach ◦ Current context of the system is used to decide what to do next ◦ Do not need a dialogue script ◦ A general engine operates on the semantic representations and the current context to control the interaction flow ► Mixed-initiative approach ◦ Not separate “user initiative” and “system initiative” Intelligent Robot Lecture Note 9

Dialog Management CMU Communicator • Dialogue management in CMU Communicator ► Frame-based approach ◦ Form-filling method ◦ Not to specify a particular order in which slots need to be filled ◦ Loosen the requirement for the system designed to correctly intuit the natural order in which information is supplied ► Agenda-based approach ◦ Treats the task as one of cooperatively constructing a complex data structure, a product ◦ Uses a product tree which is developed dynamically ◦ Supports topic shifts Intelligent Robot Lecture Note 10

Dialog Management Queen’s Communicator • Object-oriented architecture, distributed and inherited functionality: generic and domain-specific • Uses discourse history and confirmation status to determine how to confirm (explicit or implicit) Intelligent Robot Lecture Note 11

Dialog Management Dialog System Approaches Intelligent Robot Lecture Note 12

Dialog Management Dialog System approaches • There are many approaches to represent dialog ► ► Frame based Agent based Voice-XML based Information State approach Intelligent Robot Lecture Note 13

Dialog Management Frame-based Approach • Frame-based system ► ► ► Asks the user questions to fill slots in a template in order to perform a task (form-filling task) Permits the user to respond more flexibly to the system’s prompts (as in Example 2. ) Recognizes the main concepts in the user’s utterance Example 1) • System: What is your destination? • User: London. • System: What day do you want to travel? • User: Friday Intelligent Robot Lecture Note Example 2) n System: What is your destination? n User: London on Friday around 10 in the morning. n System: I have the following connection … 14

Dialog Management Frame-based Approach • Advantages ► ► The ability to use natural language, multiple slot filling The system processes the user’s over-informative answers and corrections • Disadvantages ► ► Appropriate for well-defined tasks in which the system takes the initiative in the dialog Difficult to predict which rule is likely to fire in a particular context • Related systems ► ► CU Communicator CMU Communicator Intelligent Robot Lecture Note 15

Dialog Management Agent-based Approach • Properties ► Complex communication using unrestricted natural language ► Mixed-Initiative ► Co-operative problem solving ► Theorem proving, planning, distributed architectures ► Conversational agents • Examples User : I’m looking for a job in the Calais area. Are there any servers? System : No, there aren’t any employment servers for Calais. However, there is an employment server for Pasde-Calais and an employment server for Lille. Are you interested in one of these? § System attempts to provide a more co-operative response that might address the user’s needs. Intelligent Robot Lecture Note 16

Dialog Management Agent-based Approach • Advantages ► ► Suitable to more complex dialogues Mixed-initiative dialogues • Disadvantages ► ► ► Much more complex resources and processing Sophisticated natural language capabilities Complicated communication between dialogue modules • Related Works ► ► TRAINS project TRIPS project Intelligent Robot Lecture Note 17

Dialog Management TRAINS project • TRAINS (1995~1997) ► CISD research group in University of Rochester ◦ http: //www. cs. rochester. edu/research/cisd/projects/trains/ ► Task ◦ Finding efficient routes for trains ► Goal ◦ Robust performance on a very simple task ► Approach ◦ Speech Act, Plan reasoning ► Demo ◦ http: //www. cs. rochester. edu/research/cisd/projects/trains/movies/TRAINS 9 5 -v 1. 3 -Pia. qt. gz Intelligent Robot Lecture Note 18

Dialog Management TRIPS Project • TRIPS ► The Rochester Interactive Planning System ◦ http: //www. cs. rochester. edu/research/cisd/projects/trips/ ► Goal ◦ An intelligent planning assistant (natural language + graphical display) ◦ Extending TRAINS system to several domain ► Domains (supported currently) ◦ ◦ ► Pacifica - Evacuating people from an island Airlift – Organization Airlift scheduling TRIPS-911 – Managing the resources in small 911 emergency Underwater Survey – Planning in collaboration with semiautonomous robot agents Demo (Pacifica) ◦ http: //www. cs. rochester. edu/research/cisd/projects/trips/movies/T RIPS-98_v 4. 0/200 K/TRIPS-98_v 4. 0_200 K. html Intelligent Robot Lecture Note 19

Dialog Management TRIPS Architecture The TRIPS System Architecture Intelligent Robot Lecture Note 20

Dialog Management Voice. XML-based System • What is Voice. XML? ► ► The HTML(XML) of the voice web. The open standard markup language for voice application • Can do ► ► ► Rapid implementation and management Integrated with World Wide Web Mixed-Initiative dialogue Able to input Push Button on Telephone Simple Dialogue implementation solution Intelligent Robot Lecture Note 21

Dialog Management Dialogue by Voice. XML • Most Voice. XML dialogues are built from ► ► <menu> <form> form based dialog • Form-based dialogue is similar to “Slot & Filling” system • Limiting User’s Response ► Goal ◦ Verification, and Help for invalid response ◦ Good speech recognition accuracy Intelligent Robot Lecture Note 22

Dialog Management Example - <Menu> Browser : Say one of: User Sports scores; Weather information; Log in. : Sports scores <vxml version="2. 0" xmlns="http: //www. w 3. org/2001/vxml"> <menu> <prompt>Say one of: <enumerate/></prompt> <choice next="http: //www. example. com/sports. vxml"> Sports scores </choice> <choice next="http: //www. example. com/weather. vxml"> Weather information </choice> <choice next="#login"> Log in </choice> </menu> </vxml> Intelligent Robot Lecture Note 23

Dialog Management Example – <Form> Browser : Please say your complete phone number User : 800 -555 -1212 Browser : Please say your PIN code User : 1234 <vxml version="2. 0" xmlns="http: //www. w 3. org/2001/vxml"> <form id="login"> <field name="phone_number" type="phone"> <prompt> Please say your complete phone number </prompt> </field> <field name="pin_code" type="digits"> <prompt> Please say your PIN code </prompt> </field> <block> <submit next=“http: //www. example. com/servlet/login” namelist=phone_number pin_code"/> </block> </form> </vxml> Intelligent Robot Lecture Note 24

Dialog Management Information State Approach • A method of specifying a dialogue theory that makes it straightforward to implement • Consisting of following five constituents ► Information Components ◦ Including aspects of common context ◦ (e. g. , participants, common ground, linguistic and intentional structure, obligations and commitments, beliefs, intentions, user models, etc. ) ► Formal Representations ◦ How to model the information components ◦ (e. g. , as lists, sets, typed feature structures, records, etc. ) Intelligent Robot Lecture Note 25

Dialog Management Information State Approach ► Dialogue Moves ◦ Trigger the update of the information state ◦ Be correlated with externally performed actions ► Update Rules ◦ Govern the updating of the information state ► Update Strategy ◦ For deciding which rules to apply at a given point from the set of applicable ones Intelligent Robot Lecture Note 26

Dialog Management Example Dialogue Intelligent Robot Lecture Note 27

Dialog Management Example Dialogue Intelligent Robot Lecture Note 28

Dialog Management Example Dialogue Intelligent Robot Lecture Note 29

Dialog Management Example Dialogue Intelligent Robot Lecture Note 30

Dialog Management Example Dialogue Intelligent Robot Lecture Note 31

Dialog Management Example Dialogue Intelligent Robot Lecture Note 32

Dialog Management Reading Lists • • B. Pellom, W. Ward, S. Pradhan, 2000. The CU Communicator: An Architecture for Dialogue Systems, International Conference on Spoken Language Processing (ICSLP), Beijing China. Rudnicky, A. , Thayer, E. , Constantinides, P. , Tchou, C. , Shern, R. , Lenzo, K. , Xu W. , Oh, A. 1999. Creating natural dialogs in the Carnegie Mellon Communicator system. Proceedings of Eurospeech, 531 -1534. Ian M. O’Neill and Michael F. Mc. Tear. 2000. Object-Oriented Modelling of Spoken Language Dialogue Systems Natural Language Engineering, Best Practice in Spoken Language Dialogue System Engineering, Special Issue, Volume 6 Part 3. George Ferguson and James Allen, July 1998. TRIPS: An Intelligent Integrated Problem-Solving Assistant, " in Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI -98), Madison, WI, 26 -30, pp. 567 -573. Intelligent Robot Lecture Note 33

Dialog Management Reading Lists • • S. Larsson, D. R. Traum. 2001. Information state approach to dialogue management. Current and New Directions in Discourse & Dialogue, Kluwer Academic Publishers. S. Larsson, D. R. Traum. 2003. Information state and dialogue management in the TRINDI dialogue move engine toolkit. Natural Language Engineering. Intelligent Robot Lecture Note 34

Dialog Management Dialog Modeling Techniques Intelligent Robot Lecture Note 35

Dialog Management Reinforcement Learning Training Info = desired (target) outputs Inputs (Feature, Target Label) Supervised Learning System Outputs Objective: To minimize error (Target Output – Actual Output) Training Info = evaluations (“rewards”/”costs”) Inputs (State, Action, Reward) RL System Outputs (“actions”) Objective: To get as much reward as possible Intelligent Robot Lecture Note 36

Dialog Management Stochastic Modeling Approach • Stochastic Dialog Modeling [E. Levin et al, 2000] ► Optimization Problem ◦ Minimization of Expected Cost (CD) Ci measures the effectiveness and the achievement of application goal ► Mathematical Formalization ◦ Markov Decision Process – Defining State Spaces, Action Sets, and Cost Function – Formalize dialog design criteria as objective function ► Automatic Dialog Strategy Learning from Data ◦ Reinforcement Learning Intelligent Robot Lecture Note 37

Dialog Management Mathematical Formalization • Markov Decision Process (MDP) ► ► Problems with cost(or reward) objective function are well modeled as Markov Decision Process. The specification of a sequential decision problem for a fully observable environment that satisfies the Markov Assumption and yields additive rewards. Dialog Manager Dialog State Dialog Action Cost (Prompts, Queries, etc. ) (Turn, Error, DB Access, etc. ) Environment (User, External DB or other Servers) Intelligent Robot Lecture Note 38

Dialog Management Dialog as a Markov Decision Process user dialog act user goal dialog history noisy estimate of user dialog act Speech Understanding User Reward State Estimator machine state Speech Generation Dialog Policy Reinforcement Learning Optimize MDP machine dialog act Intelligent Robot Lecture Note [S. Young, 2006] 39

Dialog Management Month and Day Example • State Space ► State St represents all the knowledge of the system at time t (values of the relevant variables). ◦ ◦ ◦ St=(d, m) where d=-1, …, 31 and m=-1, . . , 12 0 : not yet filled -1 : completely filled (0, 0) = Initial State (-1, -1) = Final State Intelligent Robot Lecture Note 40

Dialog Management Month and Day Example • State Space - Month: 1 Day: 1 - Month: 11 Day: 30 - Month: 12 Day: 31 - 1 (initial) + 12(months) + 31(days) Day: 1 Month: 1 - Day: 30 Month: 12 Day: 31 Month: 12 + 365(dates) + 1(final) Total Dialog State : 410 states Intelligent Robot Lecture Note 41

Dialog Management Month and Day Example • Action Set ► At each state, the system can choose an action at. ◦ Dialog Actions – Asking the user for input, providing a user some output, confirmation, etc. St Intelligent Robot Lecture Note Which month? (Am) Which day? (Ad) Which date? (Adm) Thank you. Good Bye. (Af) 42

Dialog Management Month and Day Example • State Transitions ► When an action is taken the system changes its state. SYSTEM : Which month? - Intelligent Robot Lecture Note Month: 11 Month: 12 New state might depend on external inputs: Not Deterministic Transition Probability: PT(St+1|St, at) 43

Dialog Management Month and Day Example • Action Costs and Objective Function ► A cost C is associated to action a at state S. t t t SYSTEM : Which month? - Intelligent Robot Lecture Note Month: 1 Cost Distribution: Pc(Ct|St, at) Month: 11 Month: 12 44

Dialog Management Month and Day Example Strategy 1. - Strategy 2. Good Bye. - Which date ? - Strategy 3. Good Bye. Day Month Which day ? - Which month? Day - Good Bye. Day Month - Optimal strategy is the one that minimizes the cost. Strategy 1 is optimal if wi + P 2* we - wf > 0 Recognition error rate is too high Strategy 3 is optimal if 2*(P 1 -P 2)* we - wi > 0 P 1 is much more high than P 2 against a cost of longer interaction Intelligent Robot Lecture Note 45

Dialog Management Policy • The goal of MDP is to learn a policy, π : S→A ► ► ► But we have no training examples of form <s, a> Training examples are of form <s, a, s’, r> For selecting it next action at based on the current observed state st. a 1 a 0 S 0 r 0 S 1 a 2 r 1 S 2 r 2 … Goal : Learn to choose actions that maximize the reward function. discount factor Intelligent Robot Lecture Note 46

Dialog Management Policy • Discounted Cumulative Reward ► Infinite-Horizon Model ◦ γ=0 : Vπ(st) =rt – Only immediate reward considered. ◦ γ closer to 1 : Delayed Reward – Future rewards are given greater emphasis relative to the immediate reward. • Optimal Policy (π*) ► Optimized policy π that maximize Vπ(s) for all state s. Intelligent Robot Lecture Note 47

Dialog Management Q-Learning • Define the Q-Function. ► As evaluation function. • Rewrite the optimal policy. • Why is this rewrite important? ► It shows that if the agent learns the Q-function instead of the V* function. ◦ It will be able to select optimal actions even when it has no knowledge of the function r and δ. Intelligent Robot Lecture Note 48

Dialog Management Q-Learning • How can Q be learned? ► Learning the Q function corresponds to learning the optimal policy. ◦ The close relationship between Q and V* ► It can be written recursively as ◦ This recursive definition of Q provides the basis for algorithm that iteratively approximate Q. ► It can updates the table entry for Q(s, a) following each such transition, according to the rule. Intelligent Robot Lecture Note 49

Dialog Management Q-Learning • Q-Learning algorithm for deterministic MDP. Intelligent Robot Lecture Note 50

Dialog Management Action Selection in Q-Learning • How actions are chosen by the agent. ► To select the action that maximize the Q hat function. ◦ Thereby exploiting its current approximation Q hat. ◦ Biased to previously trained Q hat function. ► Probability Assigning ◦ Actions with higher Q hat values are assigned higher probabilities. ◦ But every action is assigned a nonzero probability. ◦ k > 0 is a constant that determines how strongly the selection favors actions with high Q hat values. – Larger values of k will assign higher probabilities to actions with above average Q hat. – Causing the agent to exploit what it has learned and seek actions it believes will maximize its reward. ◦ k is varied with the number of iterations. – Exploitation vs. Exploration Intelligent Robot Lecture Note 51

Dialog Management Example-based Dialogue Modeling • Limitation of Rule-based Dialogue Modeling ► For the situation-action rule, there about possible 213 states of EPG domain. ◦ Problem – Much Human Efforts – Inconsistency – Unreliability • How to automatically design situation-based rules ► We have developed example-based dialogue modeling. ◦ Using dialogue examples indexed from dialogue corpus. ◦ It is more effective and domain portable. – Because it is able to automatically generate system responses from dialogue example. Intelligent Robot Lecture Note 52

Dialog Management Example-based Dialogue Modeling • Dialogue Example Database ► Semantic-based indexing of dialogue examples ◦ Lexical-based example database needs much more examples. ◦ The SLU results is the most important index key. ► Automatically indexing from dialogue corpus. Utterance 그럼 SBS 드라마는 언제 하지? Then, when do this SBS dramas start? Dialog Act Wh-question Main Action Search_start_time Component Slots [channel = SBS, genre = 드라마] Discourse History [1, 0, 0, 0, 0] System Action Inform(date, start_time, program) Intelligent Robot Lecture Note Input : User Utterance Index Keys Output : System Concept 53

Dialog Management Example-based Dialogue Modeling • Utterance Similarity ► When the retrieved dialogue examples are not unique ◦ We choose the best one using the utterance similarity measure. ► How to define the similarity measure for dialogue system. ◦ Lexico-Semantic Similarity – Morpheme Similarity between utterances with the semantic slots using normalized edit distance. ◦ Discourse History Similarity – The cosine similarity between the slot-filling vectors – The value 1 if the slot is filled until a current dialogue state. – The value 0 otherwise. Intelligent Robot Lecture Note 54

Dialog Management Example-based Dialogue Modeling • Example of Utterance Similarity ► Lexico-Semantic Representation User Utterance ► 그럼 SBS 드라마는 언제 하지? Then, when do this SBS dramas start? Component Slots [channel = SBS, genre = 드라마(drama)] Lexico-Semantic Representation 그럼 [channel] [genre] 는 언제 하 지 Then, when do the [channel] [genre] start Utterance Similarity Measure Current User Utterance 그럼 [channel] [genre] 는 언제 하 지 Slot-Filling Vector : [1, 0, 0, 0, 0] Retrieved Examples Lexico-Semantic Similarity Discourse History Similarity Intelligent Robot Lecture Note [date] [genre] 는 몇 시에 하 니 Slot-Filling Vector : [1, 0, 0, 0] 55

Dialog Management Strategy of EBDM Intelligent Robot Lecture Note 56

Dialog Management Advantages of EBDM • Generic Dialogue Modeling ► By automatically constructing the dialogue example database from the dialogue corpus • Easy Development of an effective and practical dialogue system ► Need a small amount of dialogue corpus. • High Domain Portability ► Can be applied to various domains with low cost. ◦ Goal-oriented dialogue system – EPG, Navigation, Weather Information Center ◦ Chat Agent Intelligent Robot Lecture Note 57

Dialog Management Case Study I : Example based Multi-domain Dialogue System Development Intelligent Robot Lecture Note 58

Dialog Management POSSDM • The Basic Idea ► Situation-based dialogue management ◦ State-free dialogue management based on the current situation of dialogue – Dialogue Situation is the dialogue information state. – Including user intention, semantic frame, and discourse history ► Object-oriented architecture ◦ Improving a domain portability – Separation of domain-independent and domain-dependent dialogue modules. ► Example-based dialogue modeling ◦ To generate the system responses according to the current situation using generic dialogue modeling Intelligent Robot Lecture Note 59

Dialog Management POSSDM • Overall Architecture Intelligent Robot Lecture Note 60

Dialog Management Chat Expert • Dialog Act = statement-non-opinion • Main Action= Fight • Date = 어제 Chat Dialog Corpus Dialog Act Identification USER : 어제 여친이랑 싸웠어. Frame-Slot Extraction Agent Spotter Domain Spotter • Agent = Chat • Domain = Friend Chat DEDB Discourse Inference XML Rule Parser Discourse History Stack • previous user utterance • previous dialog act and semantic frame • previous scenario session • Calculate utterance similarity Chat Expert Retrieved Dialog Examples System Response Intelligent Robot Lecture Note Chat Meta-Rule • When no example is retrieved, meta-rules are used. SYSTEM : 왜? 무슨 일 있어? 61

Dialog Management Goal-oriented Dialog Expert • Dialog Act = Wh-question Dialog Act Identification Agent Spotter USER : TV에서 지금 뭐 하지? Domain Spotter EPG Dialog Corpus • Agent = Task • Domain = EPG • Main Action= Search_Program • Start_Time = 지금 Frame-Slot Extraction (EPG) EPG DEDB Discourse Inference XML Rule Parser Discourse History Stack • previous user utterance • previous dialog act and semantic frame • previous slot-filling vector • Calculate utterance similarity EPG Expert Retrieved Dialog Examples System Response Intelligent Robot Lecture Note EPG Meta-Rule Database Manager TV Schedule Database Web Contents • When no example is retrieved, meta-rules are used. SYSTEM : 현재 “KBS”에서는 “해피선데이”가, “MBC”에서는 “일요일 밤에”가, “SBS” 에서는 “일요일이 좋다”가 방송 중 입니다. 62

Dialog Management Experiment and Result • Dialog Corpus & Experiment Setup ► ► # of Chat Corpus = 2377 user utterance in 10 domains # of Goal-Oriented Dialog Corpus = 513 user utterances in EPG and Navigation domains ► Avg. # word per utt. = 3. 22 ► Distribution of the domain in the dialog corpus Intelligent Robot Lecture Note 63

Dialog Management Experiment and Result • Spotting Evaluation ► 10 -fold cross validation using Maximum Entropy Classifier Feature Set Accuracy (%) Baseline (Only Linguistic Features) 96. 69 Semantic Features ► + Dialog Act 97. 39 + Main Action 98. 09 For the baseline performance of the domain spotter, we evaluated only using the TF*IDF weighting alone. Feature Set Accuracy(%) Baseline (TF*IDF) 72. 88 Linguistic Features 77. 47 Semantic Features 77. 92 Keyword Features Intelligent Robot Lecture Note +2 -best keyword 78. 87 +2 -best domain class 86. 18 64

Dialog Management Experiment and Result • Dialog Modeling Evaluation ► ► ► Human Evaluation: 4 test volunteers ( 422 user utterances ) EMR designates the average ratio of the example match type for user utterance input. STR designates the average success turn rate of the response correctness. The exact match means that the dialog examples were successfully retrieved when using all indexing keys. The partial match means that the dialog examples were retrieved when using parts of indexing keys after the failure of the exact match query. Intelligent Robot Lecture Note 65

Dialog Management Experiment and Result • Dialog Modeling Evaluation ► ► Example Matching Rate (EMR) and Success Turn Rate (STR) Example Match Type EMR STR Exact Match 0. 60 0. 69 Partial Match 0. 36 0. 52 No Example 0. 04 0. 06 Goal-oriented dialog evaluation of UMDM Evaluation Goal-Oriented Dialog Success Turn Rate 0. 75 Task Completion Rate 0. 81 Intelligent Robot Lecture Note 66

Dialog Management Intelligent Robot Lecture Note 67

Dialog Management Case Study II : Statistical Dialog System Design Intelligent Robot Lecture Note 68

Dialog Management Air Travel Information System (ATIS) • ATIS dialog system helps the user to find flight information in an efficient way. ► The efficiency here involves: ◦ The duration of the dialog ◦ The cost of external resources ◦ The effectiveness of the system output to the user ► Objective function <Ni> = The expected length of the whole interaction in number of turns <Nr> = The expected number of tuples retrieved from the database during the session f(No) = The data presentation cost function with No Fs = An overall task success measure f(No)= 0 k*N Intelligent Robot Lecture Note if N*< No if N*>No Fs = 1 - no info. was given 0 - otherwise N* is the reasonable value for data presentation (small for voice based system, higher for display) 69

Dialog Management The Actions in ATIS • Greeting : This is ATIS Travel service. How can I help you ? • Constraining: • Releasing constraints: Where are you departing from? (Constrain ORIGIN) What is the airline? (Constrain AIRLINE) … What time are you leaving? (Constrain DEPARTUE_TIME) There are no flight with AA. Do you want to see flight with other airlines? (Relax AIRLINE) • Database retrieval • Output data: There are 58 flights: Flight 111 leaves…, Flight 222 … • Closing: Thank you for using ATIS. Good Bye. Intelligent Robot Lecture Note . . . 70

Dialog Management The State in ATIS • State Space ► The state included three templates ◦ A template is a set of keyword-value pairs. Representing accumulated information from the user User Query History of system actions ORIGIN: X DESTINATION: X AIRLINE: X Recording a partial history of actions. Data retrieved GREETING CONSTRAINING RELAXATION … The number of data tuples retrieved Intelligent Robot Lecture Note Ndata: Y 71

Dialog Management User Model • Simulated User ► Assumption ◦ The user response depends only on the current system action and not the state. ► Parameterized the simulated user in the following way ◦ 1) Response to Greeting – P(n), n=0, 1, 2, … the # of attributes specified by the user in a single utterance. – P(attribute) (e. g. ORIGIN, DESTINATION, AIRLINE, …) – P(Value|attribute) (e. g. P(Boston|ORIGIN), P(Delta|AIRLINE)) ◦ 2) Response to Constraining Questions – P(k. R|k. G): The prob. of the user specifying a value for attribute k. R when asked for the value of attribute k. G. P(airline|departue_time) – P(N|k. G): The prob. of providing N additional unsolicited attributes in the same response. ◦ 3) Response to a Relaxation Prompt – P(yes|k. G)=1 -P(no|k. G): The prob. of accepting(or rejecting) the proposed relaxation of attribute k. G. ► We can obtain these probability distributions from dialog corpus. Intelligent Robot Lecture Note 72

Dialog Management Incrementally More Complex Strategies 1. Closing 2. Greeting Cost 1 = 1405 Retrieval Output Closing Cost 2 = 469. 24 3. Greeting Retrieval Constrain 4. Greeting Closing Output Too much data Cost 3 = 231. 95 Retrieval Closing Output No data Constrain Too much data Intelligent Robot Lecture Note Release Cost 4 = 123. 93 73

Dialog Management The Learned Optimal Strategy Greeting Constrain Enough constraints Retrieval Too much data No data Release Constrain *Same strategy was independently handcrafted in many DARPA ATIS cites: BBN, CMU, AT&T… Intelligent Robot Lecture Note Closing Output 74

Dialog Management Example of Dialog Untrained System Intelligent Robot Lecture Note Trained System 75

Dialog Management Reading Lists • • R. S. Sutton, and A. G. Barto. 1998. Reinforcement Learning: An Introduction. MIT Press S. Young. 2006. Reinforcement Learning for Spoken Dialog Systems: Using POMDPs for Dialog Management. SLT L. P. Kaelbling, M. L. Littman, and A. W. Moore. 1996. Reinforcement Learning: A Survey. Journal of Artificial Intelligence Research 4: 237 -285 E. Levin, R. Pieraccini, and W. Eckert. January 2000. A Stochastic Model of Human-Machine Interaction for Learning Dialogue Strategies. IEEE Transaction on Speech and Audio Processing. 1: 11 -23 Intelligent Robot Lecture Note 76

Dialog Management Reading Lists • • • Cheongjae Lee, Sangkeun Jung, Jihyun Eun, Minwoo Jeong, Gary Geunbae Lee. 2005. Example and situation based dialog management for spoken dialog system. Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop. Cheongjae Lee, Sangkeun Jung, Jihyun Eun, Minwoo Jeong, and Gary Geunbae Lee. 2006. A Situation-based Dialogue Management using Dialogue Examples. Proceedings of the 2006 IEEE international conference on acoustics, speech and signal processing. Cheongjae Lee, Sangkeun Jung, Minwoo Jeong, and Gary Geunbae Lee. 2006. Chat and Goal-Oriented Dialog Together: A Unified Example-based Architecture for Multi-Domain Dialog Management. Proceedings of the IEEE/ACL 2006 workshop on spoken language technology. Intelligent Robot Lecture Note 77