chatbot - apistek · dialogue system / chatbot dialogue systems are intelligent agents that are...
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ChatBot 的智慧與靈魂YUN-NUNG (VIVIAN) CHEN陳縕儂
Apr 27th, 2017@iThome #ChatBot Day
HTTP://VIVIANCHEN.IDV.TW
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Intelligent Assistants 2
Apple Siri (2011) Google Now (2012)Google Assistant (2016)
Facebook M & Bot (2015)Google Home (2016)
Microsoft Cortana (2014)
Amazon Alexa/Echo(2014)
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Why We Need?
Get things done
E.g. set up alarm/reminder, take note
Easy access to structured data, services and apps
E.g. find docs/photos/restaurants
Assist your daily schedule and routine
E.g. commute alerts to/from work
Be more productive in managing your work and personal life
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“Hey Assistant”
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Why Natural Language?
Global Digital Statistics (2015 January)
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Global Population
7.21B
Active Internet Users
3.01B
Active Social Media Accounts
2.08B
Active Unique Mobile Users
3.65B
The more natural and convenient input of devices evolves towards speech.
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Intelligent Assistant Architecture 5
Reactive Assistance
ASR, LU, Dialog, LG, TTS
Proactive Assistance
Inferences, User Modeling, Suggestions
Data Back-end Data
Bases, Services and Client Signals
Device/Service End-points(Phone, PC, Xbox, Web Browser, Messaging Apps)
User Experience“restaurant suggestions”“call taxi”
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Dialogue System / ChatBot Dialogue systems are intelligent agents that are able to help users finish tasks
more efficiently via spoken interactions.
Dialogue systems are being incorporated into various devices (smart-phones, smart TVs, in-car navigating system, etc).
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JARVIS – Iron Man’s Personal Assistant Baymax – Personal Healthcare Companion
Good dialogue systems assist users to access information conveniently and finish tasks efficiently.
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App Bot A bot is responsible for a “single” domain, similar to an app
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Seamless and automatic information transferring across domains reduce duplicate information and interaction
愛食記
Map
LINE
Goal: Schedule a lunch with Vivian
KKBOX
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GUI v.s. CUI (Conversational UI) 8
https://github.com/enginebai/Movie-lol-android
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GUI v.s. CUI (Conversational UI)
網站/APP內GUI 即時通訊內的CUI
情境 探索式,使用者沒有特定目標 搜尋式,使用者有明確的指令
資訊量 多 少
資訊精準度 低 高
資訊呈現 結構式 非結構式
介面呈現 以圖像為主 以文字為主
介面操作 以點選為主 以文字或語音輸入為主
學習與引導使用者需了解、學習、適應不同的介面操作
如同使用即時通訊軟體,使用者無需另行學習,只需遵循引導
服務入口 需另行下載App或進入特定網站與即時通訊軟體整合,可適時在對話裡提供服務
服務內容 需有明確架構供檢索 可接受龐雜、彈性的內容
人性化程度 低,如同操作機器 高,如同與人對話
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Two Branches of Bots
Task-Oriented
Personal assistant, helps users achieve a certain task
Combination of rules and statisticalcomponents
POMDP for spoken dialog systems (Williams and Young, 2007)
End-to-end trainable task-oriented dialogue system (Wen et al., 2016)
End-to-end reinforcement learning dialogue system (Li et al., 2017; Zhao and Eskenazi, 2016)
Chit-Chat
No specific goal, focus on natural responses
Using variants of generation model
A neural conversation model (Vinyals and Le, 2015)
Reinforcement learning for dialogue generation (Li et al., 2016)
Conversational contextual cues for response ranking (AI-Rfou et al., 2016)
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Dialogue System FrameworkMODULAR SYSTEM
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System Framework 12
Speech Recognition
Language Understanding (LU)• Domain Identification• User Intent Detection• Slot Filling
Dialogue Management (DM)• Dialogue State Tracking• Policy Optimization
Generation
Hypothesisare there any action movies to see this weekend
Semantic Framerequest_moviegenre=action, date=this weekend
System Action/Policyrequest_location
Text responseWhere are you located?
Screen Displaylocation?
Text InputAre there any action movies to see this weekend?
Speech Signal current bottleneck error propagation
Backend Database
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1 2
3 4
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Interaction Example
Intelligent AgentQ: How does a dialogue system process this request?
Good Taiwanese eating places include Din Tai Fung, etc. What do you want to choose?好的台式餐廳包含鼎泰豐…,你想要選哪一間?
find a good eating place for taiwanese food我想找好的台式食物
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Language Understanding
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System Framework 16
Speech Recognition
Language Understanding (LU)• Domain Identification• User Intent Detection• Slot Filling
Dialogue Management (DM)• Dialogue State Tracking• Policy Optimization
Generation
Hypothesisare there any action movies to see this weekend
Semantic Framerequest_moviegenre=action, date=this weekend
System Action/Policyrequest_location
Text responseWhere are you located?
Screen Displaylocation?
Text InputAre there any action movies to see this weekend?
Speech Signal
Backend Database
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1. Domain IdentificationRequires Predefined Domain Ontology
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Organized Domain Knowledge (Database)
Restaurant DB Taxi DB Movie DB
Classification!
find a good eating place for taiwanese food我想找好的台式食物
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find a good eating place for taiwanese food我想找好的台式食物
2. Intent DetectionRequires Predefined Schema
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Restaurant DB
FIND_RESTAURANTFIND_PRICEFIND_TYPE
:
Classification!
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find a good eating place for taiwanese food我想找好的台式食物
3. Slot FillingRequires Predefined Schema
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Restaurant DB
Restaurant Rating Type
Rest 1 good Taiwanese
Rest 2 bad Thai
: : :
FIND_RESTAURANTrating=“good”type=“taiwanese”
SELECT restaurant {rest.rating=“good”rest.type=“taiwanese”
}Semantic Frame Sequence Labeling
O O B-rating O O O B-type O
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Dialogue Management
State Tracking
Dialogue Management
Policy Optimization
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System Framework 21
Speech Recognition
Language Understanding (LU)• Domain Identification• User Intent Detection• Slot Filling
Dialogue Management (DM)• Dialogue State Tracking• Policy Optimization
Generation
Hypothesisare there any action movies to see this weekend
Semantic Framerequest_moviegenre=action, date=this weekend
System Action/Policyrequest_location
Text responseWhere are you located?
Screen Displaylocation?
Text InputAre there any action movies to see this weekend?
Speech Signal
Backend Database
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Dialogue State TrackingRequires Hand-Crafted States
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location rating type
loc, rating rating, type loc, type
all
i want it near to my office我希望可以離我辦公室近一點
NULL
find a good eating place for taiwanese food我想找好的台式食物
i want it near to my office我希望可以離我辦公室近一點
find a good eating place for taiwanese food我想找好的台式食物
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Dialogue State TrackingRequires Hand-Crafted States
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location rating type
loc, rating rating, type loc, type
all
NULL
i want it near to my office我希望可以離我辦公室近一點
find a good eating place for taiwanese food我想找好的台式食物
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Dialogue State TrackingHandling Errors and Confidence
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FIND_RESTAURANTrating=“good”type=“taiwanese”
FIND_RESTAURANTrating=“good”type=“thai”
FIND_RESTAURANTrating=“good”
location rating type
loc, rating rating, type loc, type
all
NULL
?
?
find a good eating place for taiwanese food我想找好的台式食物
rating=“good”, type=“thai”
rating=“good”, type=“taiwanese”
?
?
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Dialogue Policy for Agent Action
Inform(location=“Taipei 101”)
“The nearest one is at Taipei 101”
Request(location)
“Where is your home?”
Confirm(type=“taiwanese”)
“Did you want Taiwanese food?”
Task Completion / Information Display
ticket booked, weather information
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Generation
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System Framework 27
Speech Recognition
Language Understanding (LU)• Domain Identification• User Intent Detection• Slot Filling
Dialogue Management (DM)• Dialogue State Tracking• Policy Optimization
Generation
Hypothesisare there any action movies to see this weekend
Semantic Framerequest_moviegenre=action, date=this weekend
System Action/Policyrequest_location
Text responseWhere are you located?
Screen Displaylocation?
Text InputAre there any action movies to see this weekend?
Speech Signal
Backend Database
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Output / Natural Language Generation
Goal: generate natural language or GUI given the selected dialogue action for interactions
Inform(location=“Taipei 101”)
“The nearest one is at Taipei 101” v.s.
Request(location)
“Where is your home?” v.s.
Confirm(type=“taiwanese”)
“Did you want Taiwanese food?” v.s.
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Challenges and Recent TrendsDEEP LEARNING / MULTIMODALITY/ DIALOGUE COVERAGE / DIALOGUE COMPLEXITY
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Challenges
Predefined semantic schemaChen et al., “Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken Language Understanding,” in ACL-IJCNLP, 2015.
Data without annotationsChen et al., “Zero-Shot Learning of Intent Embeddings for Expansion by Convolutional Deep Structured Semantic Models,” in ICASSP, 2016.
Semantic concept interpretationChen et al., “Deriving Local Relational Surface Forms from Dependency-Based Entity Embeddings for Unsupervised Spoken Language Understanding,” in SLT, 2014.
Predefined dialogue statesChen, et al., “End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Language Understanding,” in Interspeech, 2016.
Error propagationHakkani-Tur et al., “Multi-Domain Joint Semantic Frame Parsing using Bi-directional RNN-LSTM,” in Interspeech, 2016.
Cross-domain intention/bot hierarchySun et al., “An Intelligent Assistant for High-Level Task Understanding,” in IUI, 2016.Sun et al., “AppDialogue: Multi-App Dialogues for Intelligent Assistants,” in LREC, 2016.Chen et al., “Leveraging Behavioral Patterns of Mobile Applications for Personalized Spoken Language Understanding,” in ICMI, 2016.
Cross-domain information transferringKim et al., “New Transfer Learning Techniques For Disparate Label Sets,” in ACL-IJCNLP, 2015.
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FIND_RESTAURANTrating=“good” rating=5? 4?
HotelRest Flight
Travel
Trip Planning
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Deep Learning for LU
IOB Sequence Labeling for Slot Filling
Intent Classification
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𝑤0 𝑤1 𝑤2 𝑤𝑛
ℎ0𝑓 ℎ1
𝑓ℎ2𝑓
ℎ𝑛𝑓
ℎ0𝑏 ℎ1
𝑏 ℎ2𝑏 ℎ𝑛
𝑏
𝑦0 𝑦1 𝑦2 𝑦𝑛
(a) LSTM (b) LSTM-LA (c) bLSTM-LA
(d) Intent LSTM
intent
𝑤0 𝑤1 𝑤2 𝑤𝑛
ℎ0 ℎ1 ℎ2 ℎ𝑛
𝑦0 𝑦1 𝑦2 𝑦𝑛
𝑤0 𝑤1 𝑤2 𝑤𝑛
ℎ0 ℎ1 ℎ2 ℎ𝑛
𝑦0 𝑦1 𝑦2 𝑦𝑛
𝑤0 𝑤1 𝑤2 𝑤𝑛
ℎ0 ℎ1 ℎ2 ℎ𝑛
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Deep Learning for LU
Joint multi-domain intent prediction and slot filling
Information can mutually enhanced
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Hakkani-Tur, et al., “Multi-Domain Joint Semantic Frame Parsing using Bi-directional RNN-LSTM,” in Interspeech, 2016.
ht-1 ht+1ht
taiwanese
B-type
food please
O O
hT+1
EOS
FIND_REST
Slot Filling Intent Prediction
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Contextual LU (Chen et al., 2016) 33
just sent email to bob about fishing this weekend
O O O OB-contact_name
O
B-subject I-subject I-subject
U
S
I send_emailD communication
send_email(contact_name=“bob”, subject=“fishing this weekend”)
are we going to fish this weekend
U1
S2
send_email(message=“are we going to fish this weekend”)
send email to bob
U2
send_email(contact_name=“bob”)
B-messageI-message
I-message I-message I-messageI-message I-message
B-contact_nameS1
Domain Identification Intent Prediction Slot Filling
Chen et al., "End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Language Understanding,” in Interspeech, 2016.
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Supervised v.s. Reinforcement
Supervised
Reinforcement
Hello
Agent
……
Agent
……. ……. ……
Bad
“Hello” Say “Hi”
“Bye bye” Say “Good bye”Learning from teacher
Learning from critics
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Scenario of Reinforcement Learning
Agent learns to take actions to maximize expected reward.
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Environment
Observation ot Action at
Reward rt
If win, reward = 1
If loss, reward = -1
Otherwise, reward = 0
Next Move
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Dialogue Policy Optimization
Dialogue management in a RL framework
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U s e r
Reward
Observation Action
Environment
Agent
Natural Language Generation Language Understanding
Dialogue Manager
Slides credited by Pei-Hao Su
Optimized dialogue policy selects the best action that can maximize the future reward.Correct rewards are a crucial factor in dialogue policy training
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Reinforcement Learning Based BotUser goal: Two tickets for “the witch” tomorrow 9:30 PM at regal meridian 16, Seattle.
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RULE BASED SYSTEMUser: Can I get 2 tickets for the witch?Agent: Which theater do you want?User: Regal meridian 16Agent: What time would you like to see it?User: 9:30 pmAgent: What date would you like to see it?User: Tomorrow!Agent: How many people are you?User: Just two adults.Agent: What is your location?User: Seattle!Agent: What kind of video format do you like?User: Oh, any kind is okay. Agent: Do you have any price constraint?User: No, I don’t care.Agent: Great - I was able to purchase 2 tickets for you to see the witch tomorrow at regal meridian 16 theater in Seattle at 9:30 pm.User: Thank you.Agent: Thank you!
REINFORCEMENT LEARNING SYSTEMUser: Can I get 2 tickets for the witch?Agent: What date would you like to see it?User: TomorrowAgent: Which theater do you prefer?User: Regal meridian 16Agent: What is your location?User: SeattleAgent: What time would you like to see it?User: 9:30 pm.Agent: Great - I was able to purchase 2 tickets for you to see the witch tomorrow at regal meridian 16 theater in Seattle at 9:30 pm.User: Thanks.Agent: Thanks!
The system can learn how to efficiently interact with users for task completion
Li et al., "End-to-End Task-Completion Neural Dialogue Systems,” in arXiv, 2017.
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Brain Signal for Understanding
Misunderstanding detection by brain signal
Green: listen to the correct answer
Red: listen to the wrong answer
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Detecting misunderstanding via brain signal in order to correct the understanding results
Sridharan et al., “NeuroDialog: An EEG-Enabled Spoken Dialog Interface,” in ICMI, 2012.
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Video for Intent Understanding 39
Proactive (from camera)
I want to see a movie on TV!
Intent: turn_on_tv
Sir, may I turn on the TV for you?
Proactively understanding user intent to initiate the dialogues.
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App Behavior for Understanding Task: user intent prediction
Challenge: language ambiguity
User preference Some people prefer “Message” to “Email” Some people prefer “Outlook” to “Gmail”
App-level contexts “Message” is more likely to follow “Camera” “Email” is more likely to follow “Excel”
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send to vivianv.s.
Email? Message?
Communication
Considering behavioral patterns in history to model understanding for intent prediction.
Chen et al., “Leveraging Behavior Patterns of Mobile Applications for Personalized Spoken Language Understanding,” in ICMI, 2015.
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Evolution Roadmap 41
Single domain systems
Extended systems
Multi-domain systems
Open domain systems
Dialogue breadth (coverage)
Dia
logu
e d
epth
(co
mp
lexi
ty)
What is influenza?
I’ve got a cold what do I do?
Tell me a joke.
I feel sad…
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Intent Expansion
Transfer dialogue acts across domains
Dialogue acts are similar for multiple domains
Learning new intents by information from other domains
CDSSM
New Intent
Intent Representation
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K:
Embedding Generation
K+1
K+2<change_calender>
Training Data<change_note>
“adjust my note”:
<change_setting>“volume turn down”
The dialogue act representations can be automatically learned for other domains
postpone my meeting to five pm
Chen et al., “Zero-Shot Learning of Intent Embeddings for Expansion by Convolutional Deep Structured Semantic Models,” in ICASSP 2016.
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Policy for Domain Adaptation
Bayesian committee machine (BCM) enables estimated Q-function to share knowledge across domains
QRDR
QHDH
QL DL
Committee Model
The policy from a new domain can be boosted by the committee policy
#dialogues
rew
ard
Gašić, et al., “Policy Committee for Adaptation in Multi-Domain Spoken Dialogue Systems,” in ASRU, 2015.
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Evolution Roadmap 44
Knowledge based system
Common sense system
Empathetic systems
Dialogue breadth (coverage)
Dia
logu
e d
epth
(co
mp
lexi
ty)
What is influenza?
I’ve got a cold what do I do?
Tell me a joke.
I feel sad…
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High-Level Intention for Dialogue Planning
High-level intention may span several domains
Schedule a lunch with Vivian.
find restaurant check location contact play music
What kind of restaurants do you prefer?
The distance is …
Should I send the restaurant information to Vivian?
Users can interact via high-level descriptions and the system learns how to plan the dialogues
Sun, et al., “An Intelligent Assistant for High-Level Task Understanding,” in IUI, 2016.
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Empathy in Dialogue System
Embed an empathy module
Recognize emotion using multimodality
Generate emotion-aware responses
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Emotion Recognizer
vision
speech
text
Fung et al., “Towards Empathetic Human-Robot Interactions,” in arXiv, 2016.
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Conclusion
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Conclusion
The conversational systems can manage information access via spoken interactions
A domain is usually constrained by the backend database
Semantic schema should be predefined
Cross-domain knowledge and intention is difficult to handled
Human-like dialogue system
Robustness error handling via interactions / reasoning
Dialogue coverage domain switching / open-domain system
Dialogue complexity common sense and empathy
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Q & ATHANKS FOR YOUR ATTENTION!
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