dialog management 1 intelligent robot lecture note

Dialog Management

Dialog Management Dialog Management

1Intelligent Robot Lecture Note

Dialog Management

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Dialog System & Architectures

Intelligent Robot Lecture Note

Dialog Management

Dialogue System

• A system to provide interface between the user and a computer-based 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


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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.


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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.

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AT&T

CMU

MIT

CU

BBN

Bell Lab

SRIDARPA


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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


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Galaxy Communicator

• The architecture


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Galaxy Communicator

• Message Passing Protocol


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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”


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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


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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)


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Dialog System Approaches


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Dialog System approaches

• There are many approaches to represent dialog► Frame based► Agent based ► Voice-XML based ► Information State approach


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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

Example 2) System: What is your destination? User: London on Friday around

10 in the morning. System: I have the following

connection …


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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


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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.


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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


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• 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/TRAINS95-v1.3-Pia.qt.gz

TRAINS project


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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 semi-

autonomous robot agents► Demo (Pacifica)

◦ http://www.cs.rochester.edu/research/cisd/projects/trips/movies/TRIPS-98_v4.0/200K/TRIPS-98_v4.0_200K.html


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TRIPS Architecture

The TRIPS System ArchitectureIntelligent Robot Lecture Note

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VoiceXML-based System

• What is VoiceXML?► 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


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Dialogue by VoiceXML

• Most VoiceXML 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


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Example - <Menu>

<vxml version="2.0" xmlns="http://www.w3.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>

Browser : Say one of: Sports scores; Weather information; Log in.

User : Sports scores


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Example – <Form>

<vxml version="2.0" xmlns="http://www.w3.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>

Browser : Please say your complete phone number

User : 800-555-1212

Browser : Please say your PIN code

User : 1 2 3 4


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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.)


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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


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Example Dialogue


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Example Dialogue


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Example Dialogue


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Example Dialogue


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Example Dialogue


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Example Dialogue


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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. McTear. 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.


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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.


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Dialog Modeling Techniques


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Reinforcement Learning

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Inputs(Feature, Target Label)

Supervised LearningSystem

Outputs

Training Info = desired (target) outputs

Objective: To minimize error (Target Output – Actual Output)

Inputs(State, Action, Reward)

RLSystem

Outputs (“actions”)

Training Info = evaluations (“rewards”/”costs”)

Objective: To get as much reward as possible


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Stochastic Modeling Approach

• Stochastic Dialog Modeling [E. Levin et al, 2000]► Optimization Problem

◦ Minimization of Expected Cost (CD)

► 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

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ii

iD CC Ci measures the effectiveness and the achievement of application goal


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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.

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Dialog Action(Prompts, Queries, etc.)

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Environment (User, External DB or other Servers)

Dialog StateCost(Turn, Error, DB Access, etc.)


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Dialog as a Markov Decision Process

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User

SpeechUnderstanding

SpeechGeneration

StateEstimator

DialogPolicy

Optimize

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usergoal

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noisy estimate ofuser dialog act

dialoghistory

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machinedialog act

ReinforcementLearning

Reward

),( mm asr

ms~

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[S. Young, 2006]


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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


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• State Space

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--

Day:1-

Day:31-

Day:30-

Day:1Month:1

Day:30Month:12

Day:31Month:12

-Month:1

-Month:12

-Month:11

--

1 (initial) + 12(months) + 31(days) + 365(dates) + 1(final)

Total Dialog State : 410 statesIntelligent Robot Lecture Note

Dialog Management


• 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.

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Which month? (Am)Which day? (Ad)Which date? (Adm)Thank you. Good Bye.(Af)

St


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• State Transitions► When an action is taken the system changes its state.

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New state might depend on external inputs:Not Deterministic

Transition Probability: PPTT(S(St+1t+1|S|Stt,a,att))

--

-Month: 12

SYSTEM : Which month?

-Month: 11

-Month: 1


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• Action Costs and Objective Function► A cost Ct is associated to action at at state St.

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Cost Distribution: PPcc(C(Ctt|S|Stt,,aatt))

slots unfilled nsinteractio *#Errors*#*#C feiD

--

-Month: 12

SYSTEM : Which month?

-Month: 11

-Month: 1


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Optimal strategy is the one that minimizes the cost.

Strategy 1 is optimal if wi + P2* we - wf > 0 Recognition error rate is too high

Strategy 1.

Strategy2.

Strategy 3.

Good Bye.

--

--

DayMonth

Which date ? Good Bye.

--

DayMonth

Day-

Which day ? Which month?

--

Good Bye.

--

--

2 11 **C fi

0 P*2* 3 23 **C fei

Strategy 3 is optimal if 2*(P1-P2)* we - wi > 0 P1 is much more high than P2 against a cost of longer interaction

0 P*2* 2 12 **C fei


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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.

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S0 S1 S2 …a0

r0

a1

r1

a2

r2

Goal : Learn to choose actions that maximize the reward function.

)10 (where ...0

22

1

iit

ittt rrrr

discount factor


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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.

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)10 (where ...)(0

22

1

iit

itttt rrrrsV

sfor )(argmax* sV


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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.

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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.

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)',(axm)('

* asQsVa

)','(ˆaxm),(ˆ'

asQrasQa

)'),,((axm),(),('

aasQasrasQa


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Q-Learning

• Q-Learning algorithm for deterministic MDP.


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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

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j

asQ

asQ

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i

k

ksaP

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),(ˆ

)|(


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Example-based Dialogue Modeling

• Limitation of Rule-based Dialogue Modeling► For the situation-action rule, there are 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.


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• 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 [1,0,1,0,0,0,0,0,0]

System Action Inform(date, start_time, program)

Input : User Utterance

Output : System Concept

Index Keys


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• 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.


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• Example of Utterance Similarity► Lexico-Semantic Representation

► Utterance Similarity Measure


User Utterance그럼 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 지Then, when do the [channel] [genre] start

그럼 [channel] [genre] 는 언제 하 지

Slot-Filling Vector : [1,0,1,0,0,0,0,0,0][date] [genre] 는 몇 시에 하 니

Slot-Filling Vector : [1,0,0,1,0,0,0,0,0]

Current User Utterance

Retrieved Examples

Lexico-Semantic Similarity

Discourse History Similarity


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Strategy of EBDM

DialogueCorpus

Dialogue Example DB

DomainExpert

User’s Utterance

AutomaticIndexing

Retrieval

DiscourseHistory

Query Generation

DialogueExamplesTie-breaking

Lexico-semantic Similarity Discourse history Similarity

Utterance Similarity

SemanticFrame

Best DialogueExample

User Intention

System Responses

DialogueCorpus

DialogueCorpus

Dialogue Example DB

DomainExpert

User’s Utterance

AutomaticIndexing

Retrieval

DiscourseHistory

Query Generation

DialogueExamplesTie-breaking





SemanticFrame

Best DialogueExample

User Intention

System Responses


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• 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

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Advantages of EBDM


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Case Study I :Example based Multi-domain

Dialogue System Development


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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


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POSSDM

• Overall Architecture


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ChatDEDB

ChatDialog Corpus

Domain Spotter

Chat Expert

Discourse History Stack

• previous user utterance• previous dialog act and semantic frame• previous scenario session

Frame-SlotExtraction

Dialog Act Identification

DiscourseInference

USER : 어제 여친이랑 싸웠어 .

USER : 어제 여친이랑 싸웠어 .

• Agent = Chat• Domain = Friend

• Dialog Act = statement-non-opinion

• Main Action= Fight• Date = 어제

Retrieved Dialog Examples

• Calculate utterance similarity

System Response SYSTEM : 왜 ? 무슨 일 있

어 ?

SYSTEM : 왜 ? 무슨 일 있어 ?

ChatMeta-Rule

XML Rule Parser

• When no example is retrieved, meta-rules are used.

Agent Spotter

Chat Expert


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EPGDEDB

EPGDialog Corpus

EPG Expert

Discourse History Stack

• previous user utterance• previous dialog act and semantic frame• previous slot-filling vector

Frame-SlotExtraction (EPG)

Dialog Act Identification

DiscourseInference

USER : TV 에서 지금 뭐 하지 ?

USER : TV 에서 지금 뭐 하지 ?

• Agent = Task• Domain = EPG

• Dialog Act = Wh-question

• Main Action= Search_Program• Start_Time = 지금

Retrieved Dialog Examples

• Calculate utterance similarity

System Response

EPGMeta-Rule

XML Rule Parser

• When no example is retrieved, meta-rules are used.

Domain SpotterAgent Spotter

SYSTEM : 현재 “ KBS” 에서는 “해피선데이”가 , “MBC” 에서는 “일요일 일요일 밤에”가 , “SBS”에서는 “일요일이 좋다”가 방송 중 입니다 .

SYSTEM : 현재 “ KBS” 에서는 “해피선데이”가 , “MBC” 에서는 “일요일 일요일 밤에”가 , “SBS”에서는 “일요일이 좋다”가 방송 중 입니다 .

Web Contents

DatabaseManager

TV ScheduleDatabase

Goal-oriented Dialog Expert


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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


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• Spotting Evaluation► 10-fold cross validation using Maximum Entropy Classifier

► For the baseline performance of the domain spotter, we evaluated only using the TF*IDF weighting alone.

Feature Set Accuracy (%)

Baseline (Only Linguistic Features) 96.69

Semantic Features+ Dialog Act 97.39

+ Main Action 98.09

Feature Set Accuracy(%)

Baseline (TF*IDF) 72.88

Linguistic Features 77.47

Semantic Features 77.92

Keyword Features+2-best keyword 78.87

+2-best domain class 86.18


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• 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.


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• Dialog Modeling Evaluation► Example Matching Rate (EMR) and Success Turn Rate (STR)

► Goal-oriented dialog evaluation of UMDM

Example Match Type EMR STR

Exact Match 0.60 0.69

Partial Match 0.36 0.52

No Example 0.04 0.06

Evaluation Goal-Oriented Dialog

Success Turn Rate 0.75

Task Completion Rate 0.81


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Case Study II : Statistical Dialog System Design


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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

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ssoorrii F)N(fNNC

<Ni> = The expected length of the whole interaction in number of turns<Nr> = The expected number of tuples retrieved from the database during the sessionf(No) = The data presentation cost function with No

Fs = An overall task success measure0 if N*< No

k*N if N*>No

f(No)=1 - no info. was given0 - otherwiseFs=

N* is the reasonable value for data presentation(small for voice based system, higher for display)Intelligent Robot Lecture Note

Dialog Management

The Actions in ATIS

• Greeting : This is ATIS Travel service. How can I help you ?

• Constraining:

• Releasing constraints:

• Database retrieval

• Output data: There are 58 flights: Flight 111 leaves…, Flight 222 …

• Closing: Thank you for using ATIS. Good Bye.

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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 youwant to see flight with other airlines? (Relax AIRLINE)

...


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The State in ATIS

• State Space► The state included three templates

◦ A template is a set of keyword-value pairs.

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User Query

History of system actions

Data retrieved

Representing accumulated information from the user

ORIGIN: XDESTINATION: XAIRLINE: X

The number of data tuples retrieved

Recording a partial history of actions.

GREETINGCONSTRAININGRELAXATION…

Ndata: YIntelligent Robot Lecture Note

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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(kR|kG): The prob. of the user specifying a value for attribute kR when asked for the value of attribute kG. P(airline|departue_time)

– P(N|kG): The prob. of providing N additional unsolicited attributes in the same response.

◦ 3) Response to a Relaxation Prompt

– P(yes|kG)=1-P(no|kG): The prob. of accepting(or rejecting) the proposed relaxation of attribute kG.

► We can obtain these probability distributions from dialog corpus.


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Incrementally More Complex Strategies

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Closing Cost1 = 14051.

Cost2 = 469.24

Greeting Retrieval Output Closing2.

RetrievalGreeting Output Closing

ConstrainToo muchdata

Cost3 = 231.95

3.

Greeting Retrieval Output Closing

Constrain Too muchdata

Release

No data

Cost4 = 123.93

4.


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The Learned Optimal Strategy

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Enoughconstraints

Greeting Constrain Retrieval

ConstrainRelease

OutputClosing

Too muchdata

No data

*Same strategy was independently handcraftedin many DARPA ATIS cites:BBN, CMU, AT&T…


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Example of Dialog

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Untrained System

Trained SystemIntelligent Robot Lecture Note

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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


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.


dialog management 1 intelligent robot lecture note

Documents

system initiative

darpa communicator program

dialogue script

dialogue main flow information

spoken dialogue systems

agendabased approach

basis dialogue manager

graph based framebased