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Pycon2017 chatbot 鄉民聊天機器人
- 1. 鄉民教我做的聊天機器⼈
- 2. 鄉民教我做的聊天機器⼈ STUDY GROUP MEMBERS ▸ Edhsu bassed1984@gmail.com ▸ Ryan ryanchao2012@gmail.com ▸ 國⽗父 joseph.yen@gmail.com ▸ 儀峰 timlmj@gmail.com ▸ 福德 zuxfoucault@yahoo.com.tw ▸ Hans costonaut@gmail.com
- 3. 鄉民教我做的聊天機器⼈ OUTLINE ▸ Background ▸ Information Retrieval Engine ▸ Demo ▸ Evaluate Retrieval Performance ▸ Ranking Considerations ▸ Q&A
- 4. BACKGROUND
- 5. 鄉民教我做的聊天機器⼈ POPULAR CHATBOTS & CHIT-CHAT BOTS 卡米狗 微軟⼩小冰Her/Him 紳⼠士狗 台股⼤大數據 捷運讚
- 6. 鄉民教我做的聊天機器⼈ WHERE TO FIND CONVERSATION DATA? ▸ It’s easy to find conversation corpus in English, such as ▸ Cornell Movie Dialogs Corpus ▸ Santa Barbara Corpus of Spoken American English ▸ Ubuntu Dialogue Corpus ▸ Microsoft Research Social Media Conversation Corpus ▸ But it's hard to find the conversation corpus in traditional Chinese. ▸ Movie subtitles (?) ▸ PTT (?)
- 7. 鄉民教我做的聊天機器⼈ MOVIE SUBTITLES ▸ ~220k Chinese Movies’ subtitles (~9M pairs) with LM filtering ▸ Seq2seq Results ▸ Unnatural ▸ Poor conversation ability
- 8. 鄉民教我做的聊天機器⼈ SO…WE TRY TO EXTRACT DATA FROM PTT ▸ After observing from PTT articles, We found the article title and comments can be paired in some “boards”.
- 10. ▸ Backend: Hosted on AWS, and used Django to manage several crawlers. ▸ About ~480k posts crawled so far. ▸ Pairing “article titles” and “comments.” Django我也略略懂略略懂 鄉民教我做的聊天機器⼈ BACKEND & PLATFORM
- 13. 鄉民教我做的聊天機器⼈ SIMILARITY RANKING ▸ Jaccard ▸ The Jaccard similarity measures similarity between finite sample sets. ▸ Example
- 14. 鄉民教我做的聊天機器⼈ BM25 ▸ Given a query “Q”, containing keywords “q”, the BM25 score of a document “D” is: ▸ Use POS-tag weighting replace “TF”
- 15. 鄉民教我做的聊天機器⼈ POS-TAG WEIGHTING + JACCARD https://gist.github.com/luw2007/6016931
- 16. 鄉民教我做的聊天機器⼈ SOME IMPROVEMENTS WE TRIED ‣ Tokenizer Improvement ‣ Emoji icon pre-processing (ex: ◢▆▅▄▃ 崩╰(〒⽫皿〒)╯潰 ▃▄▅▆◣) ‣ Improve tokenizer accuracy ‣ Keyword Extraction & Association ‣ Word2Vec (query associative term if the original one doesn’t exist)
- 17. DEMO
- 18. 鄉民教我做的聊天機器⼈ DEMO - DAILY LIFE CONVERSATION
- 21. 鄉民教我做的聊天機器⼈ MAP (MEAN AVERAGE PRECISION) ▸ “k” is the rank in the sequence of retrieved documents ▸ “P(k)” is the precision at cut-off “k” in the list.
- 23. 鄉民教我做的聊天機器⼈ VECTOR REPRESENTATION FOR DOCUMENT ▸ There’re two way to represent for document with vector ▸ Doc2Vec (gensim) ▸ RNN-encoder (arXiv:1506.08909v3) Doc2Vec RNN-encoder
- 24. 鄉民教我做的聊天機器⼈ REPRESENT RELEVANCE WITH DOC2VEC, COSINE SIMILARITY <標題>肥宅早安 :醒醒 :幾點了還睡 :早安 . . . :滾 :晚安 :早安 :我難過 :⼯三⼩ :早安 :醒了沒 :幾點了還睡 :ㄅㄅ :甲賽 :餓惹 :台灣最美的風景 :打1999檢舉 :早安 :醒醒 . .
- 25. 鄉民教我做的聊天機器⼈ NDCG (NORMALIZED DISCOUNTED CUMULATIVE GAIN) ▸ Cumulative Gain ▸ Discounted Cumulative Gain ▸ Highly relevant documents are more useful when appearing earlier in a search engine result list. ▸ Normalized DCG
- 26. ▸ Doc2Vec trick works ▸ Still have room for improvement 鄉民教我做的聊天機器⼈ COMPARE RANKING METHODS WITH NDCG
- 27. 鄉民教我做的聊天機器⼈ RANKING CONSIDERATION ▸ Ranking model: ▸ Title ranking ▸ Similarity ▸ Jaccard + POS-tag-weighting ▸ Time ▸ Comments count ▸ Doc2vec ▸ Comment ranking ▸ Length ▸ Article weighting ▸ Doc2vec
- 28. ▸ For now, It’s still very difficult to make a chatbot as a real person. So we will continue to improve in future … ▸ Resource ▸ Model ▸ Functionality 鄉民教我做的聊天機器⼈ CHALLENGE ⾰革命尚未成功，肥宅們仍須努⼒力力
- 29. Q&A