Talk by Cheng Niu, Principal NLP Engineer, WeChat Team, Tencent. As one of the biggest social network in the world, WeChat is innovating the way how people acquire the needed information, knowledge and services. In this talk, Cheng Niu presents the chatbot development effort made by WeChat AI team.

1. Intelligent Chatbot on WeChat
WeChat AI NLP Team
2017.02.08

2. 846 million monthly active users
300 million WeChat Pay users
10 million Official Accounts
200 thousand developers
WeChat is the leading mobile social network in
China. In 6 years, WeChat has gained…
Data: Tencent Financial Reports

3. WeChat is not just a mobile messaging
app. It’s a new lifestyle,
connecting people with people,
services, devices and more.
WeChat Overview
The WeChat Lifestyle

4. Red Pocket
Jan 27 – Feb 01
46 Billion
Emoji
Jan 27 – Feb 01
16 Billion
Voice Call
Jan 27 – Jan 28
2.1 Billion minutes
Chinese New Year 2017

5. 5
The new way for businesses to
interact with their customers.
Powered by WeChat

6. 7
Messaging
(Can be automated)
Account management
Service Accounts
China Merchant Bank case
China Merchants Bank
Over 10 million followers
Open an account
Pay bill/loan
Receive payment notifications
Receive CRM promotions
Powered by WeChat

7. 8
Messaging Account management
Service Accounts
China Southern Airlines case
China Southern Airlines
Buy Tickets
Check-in
Choose seats
Flight status update
Frequent flyer services
Powered by WeChat

8. Chatbot
Examples
• WeBank
• WeChat official account
• Tencent games
• Xiao‘er Mechanical Monk
Chatbot on WeChat
• Natural to server customers
• Powerful for users to acquire
service, information, knowledge,
etc.

9. Work Flow of Wechat Chatbot
Question
Question
Parsing Question
Understanding Output
Rule
Match
QnA
Chitter
Chat Model
Answer Ranking
Answer
Context
Answer
Candidates
Knowledge Base

10. Chatbot Architecture in Progress
Question
Question
Parsing Question
Understanding Output
Rule
Match
QnA
Document
Content
Chitter
Chat Model
Answer Ranking
Answer
Sentiment
Analysis
Sentiment Analysis
Output Context
Answer
Candidates
Personalization
Knowledge
Graph
Under development
• Sentiment analysis
• Knowledge graph
• Doc-chat
• Personalization
• Expose the platform to public

11. Example of Task Completion Chatbot
Intent =
Book Flight
Dialog
Manager Domain
Ontology
Slot Key Value
Intent Book Flight
Date 09-20-2016
From Beijing
To Shanghai
Date=tomorr
ow
To=Shanghai
Dialog
Manager
Slot Key Value
Intent Weather
Date 09-20-2016
Location Shanghai
Intent =
Weather
Dialog
Manager
From=Beijing
Dialog
Manager
Key Technologies:
• Intent classification
• Slot filling
• Multi-initiative context
management

12. Conversational Chatbot
How can be happy? Why I’m so busy?

13. Hard Problems for Conversational Chatbot
Question Understanding:
• 干啥呐？(what are you doing?)
• 干啥的？(what is your job?)
• 你哪里好？(why you think you are good?)
• 你在哪里? (where are you?)
• 你师父呢？(where is your master?)
• 师父在忙 (master is busy)
• 他在忙啥？ (what is he doing?)
• 闻何法啊？ (how do you practice Dharma?)
• 破除我执 (being not obsessive)
• 如何破除呢？ (how?)
Knowledge Representation:
• Notarial certificates, executed in the
mainland, and to be used in Hong Kong
Special Administrative Region, shall be
acknowledged by the Consular Department
of the Ministry of Foreign Affairs of the
People's Republic of China
• 转心 (transform the heart)，就是心里要去拿
起一个正确的东西，否则心在烦恼(affliction)
中时是很难转动的。要不断培养自己的发心
(bodhicitta-samutpada) ，让它越来越宽广，
越来越清净，烦恼自然就越来越少。恨(hatred)
也好，念(obsession)也好，都是妄想
(delusion) ，消耗心力、迷障未来。
Answer Generation: avoid trivial and
boring answers
• 忙呢 (busy now)
• 你忙 (take your time)
• 再见 (see you later)
• 狗狗很可爱 (dogs are cute）
是很可爱 (yes, they are cute)

14. Sentence Modeling by Recurrent Neural Network
x0 x1 x2 x3 xn
Embedding Layer
V0 V1 V2 V3 Vn
h0 h1 h2 hn
h3
V0 V1 V2 V3 V4 V5 V6 V7 V8
x0 x1 x2 x3 x8
Embedding Layer
x4 x5 x6 x7

15. Anaphora Resolution
Input:
q: current query
c: contenxt
Output:
q': current query after anaphora
resolution
H: replace pronouns in the current query
with noun phrases in the context
About 5% of the total queries
Examples:
C1: 你是陈奕迅粉丝吗? (are you a fan of Eason Chan? )
C2: 更喜欢张学友 (I like Jacky Cheung more)
q : 为什么更喜欢他？ (Why like him more?)
q ‘: 为什么更喜欢张学友 (Why like Jacky Cheung more?)
q'= H(q,C)
C1 : 你住哪儿? (where do you live? )
C2 : 不二寺。 (Bu’er Temple )
q : 那在哪儿？ (Where is it? )
q ‘ : 不二寺在哪儿？ (Where is Bu’er Temple ? )

16. 模型建立代消解
Context Query
陈奕迅 粉丝 更 喜欢 张学友 为什么 更 喜欢 他
)|(max 为什么更喜欢他张学友PP 
“他”(him) “张学友”(Jacky Chueng)
q' = 为什么更喜欢张学友
RNN for Anaphora Resolution
Example:
C1: 你是陈奕迅粉丝吗?
C2: 更喜欢张学友
q : 为什么更喜欢他？
q ‘: 为什么更喜欢张学友
• 100K training data
• Accuracy: 90%
• Majority of the errors are
caused by the mistakes of
entity tagging
A bad case:
C1: 你认识贤三吗?
C2: 当然认识。
q : 他是你什么人？
q ': 三是你什么人？

17. Query Complement
Input:
q: current query
c: context
Output:
q': current query after query complement
H: complete the current query with
information in the context
About 15% of the total queries
Examples:
C1: 那你会发表情包吗? (can you send emojis? )
C2: 一般不发 (usually I don’t send emojis)
q :为什么？ (Why?)
q ‘: 为什么不发表情包 (Why not send emojis?)
q'= H(q,C)
C1 :讲个故事给我听 (tell me a story )
C2 :等我学会了给你讲哦 。 (I’ll tell you a story once I learn how to)
q :我等着 (I’m waiting)
q ‘ :我等着听故事 (I’m waiting for the story)

18. 模型建立代消解
RNN for Query Complementt
Training Sample:
C1:讲个故事给我听
C2:等我学会了给你讲哦 。
q :我等着
q ‘:我等着听故事
• 100,000 training instances
• Accuracy: 70%
• Increased the engagement of
Xian’er Mechanical Monk by
11%
我 等 着 听 故
我 等 着 听
讲 个 故 事 给 我 听 _E_ 等 ...
... ...
x
y

19. 部分结果展示
你去问问师父喜欢你吗
不会的，问你师父去
什么时候问必要
Query Complement Results in Real Dialogs

20. 部分结果展示
Sentence Similarity Computation
Unsupervised word embedding approach is not good enough
Sentence 0 Sentence 1 Similarity based
on Word
Embedding
Similar
Enough?
你是谁 (who are you) 我是谁 (who am I) 0.93 No
我爱你 (I love you) 你爱我 (you love me) 0.89 No
吃饭了吗 (Do you have lunch?) 吃饭了 (just had lunch) 0.84 No
你干嘛的 (what is your job?) 你干嘛呢 (who are you doing?) 0.93 No
有轮回吗? (Is
reincarnation true?)
轮回有结束吗 (will the
cycle of life end?)
0.73 No
会不会轮回 (will
reincarnation happen?)
会不会轮回结束 (Will
reincarnation end?)
0.84 No
随喜您 (you did it well) 您做的很好 (you did it well) 0.20 Yes

21. Supervised Learning for Sentence Similarity
Feature Embedding Model
• Sentence features
unigrams
bi-grams
• Comparison Features
word pairs from two sentences each
edit operations
什么 含义 vs. 什么 意思
match-什么-什么
replace-含义-意思
RNN for sentence similarityQuestion 0 Question 1

22. Sentence Similarity Results
Models Accuracy
Unsupervised word embedding 0.63
RNN + cosine similarity 0.65
RNN + MLP 0.6878
CNN + MLP 0.6968
RNN + Tensor 0.728
Feature Embedding 0.75
220,000 sentence pairs for training
20,000 for testing

23. Response Generation
• Generative model is used if no match from knowledge base
• Neural Network based methods for response generation
24
• Motivated by neural network based methods for translation
One sentence in Language A One sentence in Language B
Input Sentence Response Sentence
Translation
Response Generation

24. Neural Network based Methods
for Response Generation
• Motivated by neural network based methods for translation
25
Training data:
Objective:

25. NN based Methods for Response Generation
26

26. Dialogue vs. Translation
27
•Dialogue corpus is different from translation
corpus
•The response diversity problem exists in
dialogue corpus

27. Diversity
28
For question: What's up?
The normal
I am OK.
I am fine.
Mr. Shelton
Bazinga!
Mr. Trump
You are fired!

28. •In our experimental corpus, more than 60 different
responses exist to the post “You are so silly”
•No!
•You are!
•Why?
•Don’t say that
•Many different responses usually correspond to the
same post
Diversity

29. Issues on Response Diversity
• Only return the safe and generic answers, i.e. the one
with the highest probability
• Cannot recognize good but low probability answers
30
Responses with high
probabilities
Good responses,
but occur not frequently
Bad responses

30. Response-Style Modeling
31

31. 32
A diverter is developed to generate the mechanism distribution of an input
post
Encoder-Diverter-Decoder

32. 34
•Training
•815, 852 pairs of post and response
•775, 852 are for training,
•40, 000 are for model validation.
•Testing
•We randomly select 300 posts from about 15 million posts
•Every baseline model generates 5 response
•Use human judgment the evaluate the model performance
Experiment

33. 36
the diversity of the response is increased by 1.7 times, and the
accuracy is increased by 9.8%
Experiment Results

34. 37
Example Output

35. Future Work
• Making use of more knowledge sources
knowledge graph
article content
• Unsupervised machine learning
• Open the service to the public
knowledge management
model tuning
chatbot customization
38

36. Voice & Audio Natural Language
Processing
Machine LearningImage & Video
WeChat AI are hiring now!
AI@tencent.com niucheng@tencent.com
Beijing, Guangzhou, Shenzhen, Palo Alto
Machine
Translation
/通用格式
/通用格式
/通用格式
/通用格式
/通用格式
/通用格式 /通用格式 /通用格式 /通用格式 /通用格式
samples/sec
batch_size
speed, 4 gpus
amber mxnet tf

37. Thanks
WeChat A.I. NLP