This document describes a summer internship project using chatbots to sell products online. The intern developed a neural sequence-to-sequence model with attention to generate sentences explaining the attractiveness of travel spots in Japan based on their features. The model was trained on over 144,000 sentences from a travel curation website describing spots and reasons to visit them. It can generate sentences explaining why a spot is recommended based on its features, or reverse the input and output to explain the features based on the reasons. The intern believes attention mechanisms work well and future work could involve linking the model to a knowledge base or using reinforcement learning with user feedback.

1. 対話における商品の営業
佐藤 元紀｜Motoki
Sato
対 話 E C
NAIST
(M1)
Preferred
Networks
Summer
Internship,
2016
(Mentors:
Unno-­‐san,
Fukuda-­‐san)

2. Introduction
l Recently,
chat-­‐bots
are
used
in
many
field.
l Chat-­‐bot will
be
used
to
sell
products
online.
2
Background
My
Internship
theme
l Explain
why
this
product
is
recommended
to
you.
l Generate
sentences
which explain
attractiveness
of
products.
商品の
特徴⽂文
ユーザに
おすすめな
理理由⽂文
Inference…?
Product
feature
sentence
Reason
sentence

3. Data 3
l Find
Travel (Curation
web
site
in
travel
domain)
l Articles
have
many
attractive
spots
in
Japan.
l Spot
Data
:
67,477
(spot,
hotel,
cafe)
Spot
name description information Image
URL
http://find-­‐travel.jp/

4. Data Processing 4
l Split text
to
sentences.
l Extract
“reasoning
sentence” include
word
“なので” or
“ので”
(heuristic)
– Number
of
sentences
:
144,032
– Sentence
Examples
:
User
Value
Fact
(spot
feature
sentence)
→
なので

5. Model
l Sequence-­‐to-­‐Sequence Model
with
Attention
[Cho
et
al.,
2014,
Bahdanau et
al.,
2014]
l We
train
two
difference
networks.
(1.
Normal
and
2.Reverse)
5
Input:
output:
Hidden
unit 400
Network 1
layer
bi-­‐LSTM
Batch
size 100
Optimizer Adam
User
Value
Fact
1.
Normal
Input:
output:
User
Value
Fact
2.
Reverse

6. DEMO (1) 6Normal
(A
→ B)
Input Output
Attention
Examples:

7. DEMO (2) 7Reverse
(A
← B)
OutputInput
Attention
Examples:

8. DEMO (3) Spot Search 8
Vector
Space Paragraph
Vector
(Skip-­‐gram
like)
Epoch 500
Window
size 15
Optimizer SGD

9. Conclusions
l We
build
Neural
Sequence-­‐to-­‐Sequence
model
to
explain
product
by
sentence.
l Attention
alignment
work
so
good
l Attention
with
Databese or
Knowledge
Base
[Pengcheng Yin,
2016]
(QA)
Pengcheng Yin,
Zhengdong Lu,
Hang
Li,
Ben
Kao.
“Neural
Enquirer:
Learning
to
Query
Tables
with
Natural
Language”
IJCAI
2016
l Spot
search
using
Reinforcement
Learning
(user
feedback
signal)
9
Future
Work