Chat bot made by the chainer chainer is the neural network framework Japanese sentence remove. I don't know the reason why removing the Japanese sentence This presentation material is the Pycon 2016 ******************************************* 大串　正矢 Ogushi Masaya Twitter:https://twitter.com/SnowGushiGit Qiita:http://qiita.com/GushiSnow Github:https://github.com/SnowMasaya *******************************************

1. Chat Bot Made by Chainer
Chainer is a Neural Network Framework
PyCon JP 2016
Masaya Ogushi
1

2. Attention
I will not show any Mathematical formula
If you understand the machine learning
model, I recommend to read the paper in
the last page
2

3. Agenda
：
Self Introduction
Dialogue Value
Character of the Bot
System
Feature Plan
3
Chat Bot
Choose
the Topic
Understand
Contents Control
Dialogue
Generate
Answer
Answer
Candidates
Neural Network１
Understand
Contents Control
Dialogue
Generate
Answer
Neural Network 2
Question
Question Answer

4. Self Introduction
4

5. Self Introduction
Name：Masaya Ogushi
@SnowGushiGit
PORT. Inc
Web Development
Research and Development team
Tech-Circle staff
machine learning, Natural Language
Processing, Crawler Dev, Automatic
Infrastructure Construction, parallel
processing, SearchFunction
5

6. Self Introduction
We’re Hiring!!
https://www.theport.jp/recruit/information/
6

7. Self Introduction
Free Consulting for your job search
https://port-recruitment.jp/
7

8. Dialogue Value
8

9. Agenda
：
Self Introduction
Dialogue Value
Character of the Bot
System
Feature Plan
9
Chat Bot
Choose
the Topic
Understand
Contents Control
Dialogue
Generate
Answer
Answer
Candidates
Neural Network１
Understand
Contents Control
Dialogue
Generate
Answer
Neural Network 2
Question
Question Answer

10. Dialogue Value
Dialogue Value
Continuously
Interactive
New User Experiences
10

11. Dialogue Value
Continuously
It is possible to use the
prior conversation’s
information
11
I really love to
play the tennis
Chat Bot
That’s sounds
great
Well,
a friend of mine owns
a sports shop and is
looking for help.
find the part time job
candidates
Yeah well
I looking for the
part time job
Do you know the
any good ones?

12. Dialogue Value
Interactive
It is possible to react to
new information
12
I found the
delicious sweets
I am on a diet
Don’t say such a
things during the
I’m on a diet
This food is a
good for losing
the weight
Really ???
Chat Bot

13. Dialogue Value
New User Experiences
Character
13
Example

14. Dialogue Value
Dialogue Value
continuously
Interactive
New User Experiences
14
Need Dialogue
Data
Possible to achieve
even without
dialogue data

15. Character of the Bot
15

16. Agenda
：
Self Introduction
Dialogue Value
Character of the Bot
System
Feature Plan
16
Chat Bot
Choose
the Topic
Understand
Contents Control
Dialogue
Generate
Answer
Answer
Candidates
Neural Network１
Understand
Contents Control
Dialogue
Generate
Answer
Neural Network 2
Question
Question Answer

17. Which Characters looks smart ？
17
Character of the Bot

18. Which Characters would you like to talk with？
18
Character of the Bot

19. Which looks and character is a big gap ?
19
Character of the Bot
I read the
“Statistical Machine
Translation”
I read the
“Statistical Machine
Translation”

20. Character of the Bot
I recommend
Matsuya
Do you know any
good part time
jobs
No, something
more suited to
me
How about a
sweets shop ?
Sounds Good
Character is very important
20
Food Shop
Maybe
Sweets are
the better
Looks Funny
Uhhh.. He
only think
about food.
You understand
it better than I
expected
Learning
allowance
Surprise
Chat Bot

21. Character of the Bot
Improve the new use
experiences
Decreasing the expected
value of the answer and
Became easer to talk to
Image of the Icon
Conversation of the
Bot
21
Cognitive Science
Expect value of the
answer：High
Easy to talk：Low
Expect value of the
answer：Low
Easy to talk：High
Feel free to talk
to me
Feel free to talk
to me
Talk it

22. Character of the Bot
Preparing the sentences
for each character is costly
We have to change a little
in the same sentences
*The Example uses the
different levels of
politeness in Japanese, the
nuances of which are hard
to translate into English
22
あなたは食べたパン
の数を
把握されていますか？
お前は食ったパンの
数を
覚えているか？
あなたは食べたパン
の数を
覚えているの？

23. 23
Character of the Bot
We need Many scenario writers

24. Character of the Bot
We would like to change
the Character but not
change the contents
24
あなたは食べたパン
の数を
把握されていますか？
お前は食ったパンの
数を
覚えているか？
あなたは食べたパン
の数を
覚えているの？

25. Character of the Bot
25
Woman
Fat Man
Steward
Do you remember the
number of the breads ?
Add the
Character

26. Character of the Bot
NeuralStoryTeller
Add the Character into the normal sentences。Add the romantic elements below
26

27. 27
Character of the Bot
It is possible to apply to a variety of situations, if we
prepare the characters sentences

28. 28
Character of the Bot
I’m sorry.
I can’t implement this characters function.

29. Agenda
：
Self Introduction
Dialogue Value
Character of the Bot
System
Feature Plan
29
Chat Bot
Choose
the Topic
Understand
Contents Control
Dialogue
Generate
Answer
Answer
Candidates
Neural Network１
Understand
Contents Control
Dialogue
Generate
Answer
Neural Network 2
Question
Question Answer

30. System
30

31. Agenda
：
System Architecture
Dialogue Interface is Slack
Prepare the conversation data form Twitter
Pre training use the Wikipedia Data and the Dialogue Breakdown Collection
Choose the TOPIC by using the WordNet and WikiPedia Entity Vector
Dialogue model made by Chainer
Question and Answer functionally uses Elasticsearch
31
Chat Bot
Choose
the Topic
Understand
Contents Control
Dialogue
Generate
Answer
Answer
Candidates
Neural Network１
Understand
Contents Control
Dialogue
Generate
Answer
Neural Network 2
Question
Question Answer
WordNet
WikiPediaVector

32. Agenda
：32
Chat Bot
Choose
the Topic
Understand
Contents Control
Dialogue
Generate
Answer
Answer
Candidates
Neural Network１
Understand
Contents Control
Dialogue
Generate
Answer
Neural Network 2
Question
Question Answer
WordNet
WikiPediaVector
Choose the Topic

33. System
Choose the Topic
Conversation contents is changed by the someone
33
Which is
more important
to you
me or your job
Please tell
me where
you bought
your clothes
Please give
me a money
Boy
Friend
Young
Sister
Father

34. System
Choose the Topic
Word Net
Data set is the grouped into set of cognitive synonyms, each
expressing a distinct concept
34
Scottish hold Black cat Orange Cat
Cat

35. 35
System
Many concept. 57238 concepts
It is difficult to prepare the data.

36. 36
System
Grouping the concepts

37. System
The way of grouping
Mapping the concept space
Grouping by distance
37
cat
cat
cat
Tiger

38. System
Mapping the concept space
38
Facebook
Twitter
Close？？
We could not understand
the distance comparing
each words
We have to map the
word to space, which
makes it possible to
measure the distance

39. System
Mapping the concept space
Entity Linking
Mapping the Keyword to the Knowledge space
39
Facebook
Twitter
Close？？
Knowledge
Space Facebook
Twitter
SNS

40. System
Choose the Topic
Mapping the concept to the knowledge space
Japanese WikiPedia Entity Vector ！！！
Vector representations of Words and WikiPedia(Knowldge)
（Wikipedia is the called the Entity）
40

41. System
Choose the Topic
Synonym get the vector by the WikiPedia Entity Vector
41
cat:[0.2, 0.3, 0.4…] dog:[0.3, 0.4, 0.5…]

42. 42
System
Measure the Distance

43. System
Measuring the Concept Distance
43
Choose the appropriate
measure for the
distance in mapping
space
If we make a mistake
choosing the
measuring of the
distance.
It
looks yellow
is close
Light blue is
closer than
yellow

44. 44
System
I used cosine similarity to measure the
vector distance

45. System
Choose the Topic
Synonym get the vector by the WikiPedia Entity Vector
45
Cat:[0.2, 0.3, 0.4…] Dog:[0.3, 0.4, 0.5…]
Cosine Similarity

46. 46
System
Many Concepts yet

47. System
Choose the Topic
Add the Unknown words of the WordNet from the Wikipedia Entity Vector.
47
Black Cat
White Cat
：
calico cat
：
CatWikipedia Entity
Vector
Close the
Cosine
Similarity
Add the
Unknown words
Word Net

48. System
Choose the Topic
Calculate the each concept Average vector
48
Black Cat:[0.2, 0.3, 0.4…]
White Cat:[0.1, 0.3, 0.…]
：
Cat
Shiba:[0.1, 0.3, 0.4…]
Tosa:[0.1, 0.2, 0.…]
：
Dog
Average Vector
Average Vector

49. System
Choose the Topic
If the average vector is close to each concept, group them by concept
49
Black Cat:[0.2, 0.3, 0.4…]
White Cat:[0.1, 0.3, 0.…]
：
Cat
Shiba:[0.1, 0.3, 0.4…]
Tosa:[0.1, 0.2, 0.…]
：
Dog
Average Vector
Average Vector
grouping the each
concept

50. 50
System
Many concepts yet（20000 concepts）

51. System
Choose the Topic
Choose the concept from over the 1000 words. It is easy to match the phrase.
51
Black Cat
White Cat
：
Cat
Shiba
Tosa
：
Dog
swan
duck
：
Bird
koala
Koala
Choosing the Concept

52. 52
System
76 concepts
（Attention：I didn’t use the all WikiPedia Entity Vectors）

53. System
Choose the Topic
The way of the choosing the dialogue
Choose the each concept by the word match rate
53
Where
can I buy
cute
clothes ?
Boy Friend
Cool
Nice guy
：
Young Sister
Cute
Clothes
：
Father
money
gentle
：
Calculate the
word match
rate

54. Agenda
：54
Chat Bot
Choose
the Topic
Understand
Contents Control
Dialogue
Generate
Answer
Answer
Candidates
Neural Network１
Understand
Contents Control
Dialogue
Generate
Answer
Neural Network 2
Question
Question Answer
WordNet
WikiPediaVector
Understand Contents
Control Dialogue
Generate the Answer

55. 55
System
All parts made by the Neural Network
(Attention：I might be the best way）

56. 56
System
Why Neural Network?
I will explain how to apply the Neural Network to
Natural Language Processing

57. Dialogue Value
Value of the Neural Network
Expression
Continuously
Focus
57

58. Dialogue Value
Value of the Neural Network
Expression
Continuously
Focus
58

59. 59
System
Mapping natural language to the vector space using the Bag of
words
（Prepare the Dictionary and Count the word in the dictionary）
Low High
Word Phrase sentenceExpression
I show am me your you … when are
1, 0, 0, 0, 0, 0, … 0, 0I
am
Shota
I show am me your you … when are
0, 0, 1, 0, 0, 0, … 0, 0
I show am me your you … when are
0, 0, 0, 0, 0, 0, … 0, 0
It is rate time to use it, but over the million wordsData

60. 60
System
It only considers words.

61. 61
System
Deep Learning is an efficient method for learning high-quality
distributed vector representations that capture a large
number of precise syntactic and semantic word relationships
Low High
Word Phrase sentenceExpression
I
am
Shota
Distributed representations of words in a vector
space by the Deep leaning
Data
Deep Learning
0.5, 0.0, 1.0, 1.0, 0.3, 0.0
0.5, 0.0, 1.0, 1.0, 0.0, 0.0
0.5, 0.0, 1.0, 0.5, 0.3, 0.0

62. Dialogue Value
Value of the Neural Network
Expression
Continuously
Focus
62

63. 63
System
太郎 さん こんにちは
Phrase is important for Continuously
Recurrent Neural Network is possible to consider the
Continuously

64. Dialogue Value
Value of the Neural Network
Expression
Continuously
Focus
64

65. 65
System
+
太郎 さん こんにちは
Focus is important for important phrasing.
A Attention Model(Neural Network) considers which are
the focus words

66. System
Value of the Neural Network
Expression
Continuously
Focus
66
Which is more
important
to you
me or your job
Please tell
me where
you bought
your clothes
Please give
me money
Boy
Friend
Young
Sister
Father

67. 67
System
How to implement a Neural Network

68. 68
System
This is a Dialogue Model
太郎 さん こんにちは
こんにちは<EOS>
+Encoder
Decoder

69. 69
System
Mapping the Phrases to a neural network space.
The middle layer express a neural network space.
太郎 さん こんにちは
太郎:1
さん:0
こんにちは:0
:
太郎:1
さん:0
こんにちは:0
:

70. 70
System
Continuously learn from phrases
0
0
0
0
1
:
0 output
layer
さん
こんにちはhidden
layer
太郎の時
の
隠れ層
Transform Matrix
Copy the past value
太郎 さん こんにちは

71. 71
System
The input sentence is reversed.
The first word is the most important.
太郎 さん こんにちは

72. 72
System
Forward Information and Reverse Information are
Convolution
+
Encoder
太郎 さん こんにちは

73. 73
System
Generating the phrases by the Convolution information
太郎 さん こんにちは
+Encoder
こんにちは
Decoder

74. 74
System
It is consider the continuously Generating the Phrases
太郎 さん こんにちは
こんにちは<EOS>
+Encoder
Decoder

75. 75
System
The Value of this model
Expression
Continuously
Focus

76. Agenda
：76
Chat Bot
Choose
the Topic
Understand
Contents Control
Dialogue
Generate
Answer
Answer
Candidates
Neural Network１
Understand
Contents Control
Dialogue
Generate
Answer
Neural Network 2
Question
Question Answer
WordNet
WikiPediaVector
Question and Answer Function

77. 77
System
How to know if it’s a question or not

78. System
It is very simple to decide
Is there a question mark (?)
If you interested in detecting questions, I recommend you read the paper below
Li, Baichuan, et al. "Question identification on twitter." Proceedings of the 20th ACM international conference on Information
and knowledge management. ACM, 2011.
78
Where
can I buy
cute
clothes ?
Understand
Contents Control
Dialogue
Generate
Answer
Neural Network１
Question Answer
Please tell
me where
I can find
cute
clothes

79. 79
System
Demonstration！！
https://youtu.be/ulICnU2f2Po

80. Agenda
：
Self Introduction
Dialogue Value
Character of the Bot
System
Feature Plan
80
Chat Bot
Choose
the Topic
Understand
Contents Control
Dialogue
Generate
Answer
Answer
Candidates
Neural Network１
Understand
Contents Control
Dialogue
Generate
Answer
Neural Network 2
Question
Question Answer

81. Feature Plan
81

82. Feature Plan
Prepare the enough test
Not Enough test code
Evaluation
F measure
Apply the latest Chainer
I hear the Trainer function is good
Rule base and Neural Network
NeuralStoryTeller
Add the character
82

83. Conclusion
Word Net is a Concept Dataset
It is possible to find other data which express the concept
Mapping words to Vector space using Wikipedia Entity Vectors
We make the Vector spaces using our own data set
Hybrid function (Neural Network and Rule based)
Please search github for “Chainer Slack Twitter”
Please give me a star
I prepare the Docker Container
please search for “Docker hub Chainer-Slack-Twitter-Dialogue”
83

84. Conclusion
We’re Hiring!!
https://www.theport.jp/recruit/information/
84

85. Reference
• Chainerで学習した対話用のボットをSlackで使用+Twitterから学習データを取得してファインチューニン
• http://qiita.com/GushiSnow/items/79ca7deeb976f50126d7
• WordNet
• http://nlpwww.nict.go.jp/wn-ja/
• 日本語 Wikipedia エンティティベクトル
• http://www.cl.ecei.tohoku.ac.jp/~m-suzuki/jawiki_vector/
• PAKUTASO
• https://www.pakutaso.com/
• Luong, Minh-Thang, Hieu Pham, and Christopher D. Manning. "Effective approaches to attention-based neural
machine translation." arXiv preprint arXiv:1508.04025 (2015).
• Rush, Alexander M., Sumit Chopra, and Jason Weston. "A neural attention model for abstractive sentence
summarization." arXiv preprint arXiv:1509.00685 (2015).
• Tech Circle #15 Possibility Of BOT
• http://www.slideshare.net/takahirokubo7792/tech-circle-15-possibility-of-bot
• Generating Stories about Images
• https://medium.com/@samim/generating-stories-about-images-d163ba41e4ed#.h80qhbd54
• 二つの文字列の類似度
• http://d.hatena.ne.jp/ktr_skmt/20111214/1323835913
• Li, Baichuan, et al. "Question identification on twitter." Proceedings of the 20th ACM international conference
on Information and knowledge management. ACM, 2011.
• 音源：スカイウォーキング
• http://dova-s.jp/bgm/download5052.html
• 音源：get into the rhythm
• http://dova-s.jp/bgm/download5145.html
• 構文解析
• http://qiita.com/laco0416/items/b75dc8689cf4f08b21f6
85