Chatbot的智慧與靈魂

ChatBot 的智慧與靈魂
YUN-NUNG (VIVIAN) CHEN 陳縕儂
Apr 27th, 2017
@iThome #ChatBot Day
HTTP://VIVIANCHEN.IDV.TW

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)

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
3
“Hey Assistant”

Why Natural Language?
 Global Digital Statistics (2015 January)
4
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.

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”

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).
6
JARVIS – Iron Man’s Personal Assistant Baymax – Personal Healthcare Companion
Good dialogue systems assist users to access information conveniently and finish tasks efficiently.

App  Bot
 A bot is responsible for a “single” domain, similar to an app
7
Seamless and automatic information transferring across domains
 reduce duplicate information and interaction
愛食記
Map
LINE
Goal: Schedule a lunch with Vivian
KKBOX

GUI v.s. CUI (Conversational UI) 8
https://github.com/enginebai/Movie-lol-android

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
9

Two Branches of Bots
Task-Oriented
 Personal assistant, helps users achieve a
certain task
 Combination of rules and statistical
components
 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)
10

Dialogue System Framework
MODULAR SYSTEM
11

System Framework 12
Speech Recognition
Language Understanding (LU)
• Domain Identification
• User Intent Detection
• Slot Filling
Dialogue Management (DM)
• Dialogue State Tracking
• Policy Optimization
Generation
Hypothesis
are there any action movies to
see this weekend
Semantic Frame
request_movie
genre=action, date=this weekend
System Action/Policy
request_location
Text response
Where are you located?
Screen Display
location?
Text Input
Are there any action movies to see this weekend?
Speech Signal current bottleneck
 error propagation
Backend Database

Interaction Example
Intelligent Agent
Q: 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
14

System Framework 16
Speech Recognition
• Slot Filling
Generation
Hypothesis
see this weekend
Semantic Frame
request_movie
request_location
Text response
Screen Display
location?
Text Input
Speech Signal
Backend Database

1. Domain Identification
Requires Predefined Domain Ontology
17
Organized Domain Knowledge (Database)
Restaurant DB Taxi DB Movie DB
Classification!

2. Intent Detection
Requires Predefined Schema
18
Restaurant DB
FIND_RESTAURANT
FIND_PRICE
FIND_TYPE
:
Classification!

3. Slot Filling
Requires Predefined Schema
19
Restaurant DB
Restaurant Rating Type
Rest 1 good Taiwanese
Rest 2 bad Thai
: : :
FIND_RESTAURANT
rating=“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

Dialogue Management
State Tracking
Dialogue Management
Policy Optimization

System Framework 21
Speech Recognition
• Slot Filling
Generation
Hypothesis
see this weekend
Semantic Frame
request_movie
request_location
Text response
Screen Display
location?
Text Input
Speech Signal
Backend Database

Dialogue State Tracking
Requires Hand-Crafted States
22
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

Requires Hand-Crafted States
23
all
NULL

Handling Errors and Confidence
24
FIND_RESTAURANT
rating=“good”
FIND_RESTAURANT
rating=“good”
type=“thai”
FIND_RESTAURANT
rating=“good”
all
NULL
?
?
rating=“good”,
type=“thai”
rating=“good”,
?
?

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
25

System Framework 27
Speech Recognition
• Slot Filling
Generation
Hypothesis
see this weekend
Semantic Frame
request_movie
request_location
Text response
Screen Display
location?
Text Input
Speech Signal
Backend Database

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.
28

Challenges and Recent Trends
DEEP LEARNING / MULTIMODALITY/ DIALOGUE COVERAGE / DIALOGUE COMPLEXITY
29

Challenges
 Predefined semantic schema
Chen et al., “Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken Language Understanding,” in ACL-IJCNLP, 2015.
 Data without annotations
Chen et al., “Zero-Shot Learning of Intent Embeddings for Expansion by Convolutional Deep Structured Semantic Models,” in ICASSP, 2016.
 Semantic concept interpretation
Chen et al., “Deriving Local Relational Surface Forms from Dependency-Based Entity Embeddings for Unsupervised Spoken Language Understanding,” in SLT, 2014.
 Predefined dialogue states
Chen, et al., “End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Language Understanding,” in Interspeech, 2016.
 Error propagation
Hakkani-Tur et al., “Multi-Domain Joint Semantic Frame Parsing using Bi-directional RNN-LSTM,” in Interspeech, 2016.
 Cross-domain intention/bot hierarchy
Sun 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 transferring
Kim et al., “New Transfer Learning Techniques For Disparate Label Sets,” in ACL-IJCNLP, 2015.
30
FIND_RESTAURANT
rating=“good” rating=5? 4?
HotelRest Flight
Travel
Trip
Planning

Deep Learning for LU
 IOB Sequence Labeling for Slot Filling
 Intent Classification
31
𝑤0 𝑤1 𝑤2 𝑤 𝑛
ℎ0
𝑓
ℎ1
𝑓
ℎ2
𝑓
ℎ 𝑛
𝑓
ℎ0
𝑏
ℎ1
𝑏
ℎ2
𝑏 ℎ 𝑛
𝑏
𝑦0 𝑦1 𝑦2 𝑦 𝑛
(a) LSTM (b) LSTM-LA (c) bLSTM-LA
(d) Intent LSTM
intent
ℎ0 ℎ1 ℎ2 ℎ 𝑛

Deep Learning for LU
 Joint multi-domain intent prediction and slot filling
 Information can mutually enhanced
32
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

Contextual LU (Chen et al., 2016) 33
just sent email to bob about fishing this weekend
O O O O
B-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-message
I-message
I-message I-message I-message
I-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.

Supervised v.s. Reinforcement
 Supervised
 Reinforcement
Hello 
Agent
……
Agent
……. ……. ……
Bad
“Hello” Say “Hi”
“Bye bye” Say “Good bye”
Learning from teacher
Learning from critics
34

Scenario of Reinforcement Learning
Agent learns to take actions to maximize expected reward.
35
Environment
Observation ot Action at
Reward rt
If win, reward = 1
If loss, reward = -1
Otherwise, reward = 0
Next Move

Dialogue Policy Optimization
 Dialogue management in a RL framework
36
U s e r
Reward
ObservationAction
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

Reinforcement Learning Based Bot
User goal: Two tickets for “the witch” tomorrow 9:30 PM at regal meridian 16, Seattle.
37
RULE BASED SYSTEM
User: Can I get 2 tickets for the witch?
Agent: Which theater do you want?
User: Regal meridian 16
Agent: What time would you like to see it?
User: 9:30 pm
Agent: 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 SYSTEM
User: Can I get 2 tickets for the witch?
Agent: What date would you like to see it?
User: Tomorrow
Agent: Which theater do you prefer?
User: Regal meridian 16
Agent: What is your location?
User: Seattle
Agent: 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.

Brain Signal for Understanding
 Misunderstanding detection by brain signal
 Green: listen to the correct answer
 Red: listen to the wrong answer
38
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.

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.

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”
40
send to vivian
v.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.

Evolution Roadmap 41
Single
domain
systems
Extended
systems
Multi-
domain
systems
Open
domain
systems
Dialogue breadth (coverage)
Dialoguedepth(complexity)
What is influenza?
I’ve got a cold what do I do?
Tell me a joke.
I feel sad…

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
1
2
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.
42

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
reward
Gašić, et al., “Policy Committee for Adaptation in Multi-Domain Spoken Dialogue Systems,” in ASRU, 2015.
43

Evolution Roadmap 44
Knowledge based system
Common sense system
Empathetic systems
Dialogue breadth (coverage)
Dialoguedepth(complexity)
What is influenza?
I’ve got a cold what do I do?
Tell me a joke.
I feel sad…

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.
45

Empathy in Dialogue System
 Embed an empathy module
 Recognize emotion using multimodality
 Generate emotion-aware responses
46
Emotion Recognizer
vision
speech
text
Fung et al., “Towards Empathetic Human-Robot Interactions,” in arXiv, 2016.

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
48

Q & A
THANKS FOR YOUR ATTENTION!
49

Chatbot的智慧與靈魂

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