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対話における商品の営業

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Motoki Sato, PFN Summer Internship 2016

Published in: Technology
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対話における商品の営業

  1. 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. 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. 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. 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. 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. 6. DEMO  (1) 6Normal        (A  → B) Input Output Attention  Examples:
  7. 7. DEMO  (2) 7Reverse        (A  ← B) OutputInput Attention  Examples:
  8. 8. DEMO  (3)    Spot  Search 8 Vector  Space Paragraph  Vector (Skip-­‐gram  like) Epoch 500 Window  size 15 Optimizer SGD
  9. 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

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