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NN時代の自然言語処理の設計と評価

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全脳アーキテクチャ若手の会 - 自然言語処理の最先端(https://wbawakate.connpass.com/event/88702/)の発表資料です

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NN時代の自然言語処理の設計と評価

  1. 1. NN 135 WBA -
  2. 2. Koki Yasuda(@himanandayonaxa) : (B4) : , , : NLP : nltk 235 WBA -
  3. 3. NN ▼ github github.com/yasudadesu/nlp_analayze 335 WBA -
  4. 4. 1. NLP NN 2. NLP 3. 4. 5. NN 435 WBA -
  5. 5. NN 535 WBA - 1. NLP NN 2. NLP 3. 4. 5.
  6. 6. 635 WBA - NLP NN
  7. 7. NLP ACL 2011-2018 www.aclweb.org/ EMNLP 2011-2017 emnlp2017.net/ NAACL HLT 2013, 15, 16, 18 naacl2018.org/ 735 WBA - ▼
  8. 8. ACL (Association for Computational Linguistics) year_w … year_r … accepted paper 835 WBA -
  9. 9. 935 WBA - year_w … year_r … accepted paper ACL (Association for Computational Linguistics)
  10. 10. EMNLP (Empirical Methods in Natural Language Processing) 1035 WBA -
  11. 11. NAACL HLT (North American Chapter of the Association for Computational Linguistics: Human Language Technologies) 1135 WBA -
  12. 12. 1235 WBA - NLP NN ’neural’ ’model’
  13. 13. 1335 WBA - NLP NN Recent Trends in Deep Learning Based Natural Language Processing Tom et al. arxiv.org/pdf/1708.02709.pdf NN 70%
  14. 14. 1. NLP NN 2. NLP 3. 4. 5. NN 1435 WBA -
  15. 15. 1535 WBA - Slideshare -
  16. 16. 1635 WBA - Slideshare -
  17. 17. 1735 WBA -
  18. 18. 1835 WBA - N-gram (Embedding) RNN, CNNSVM NN
  19. 19. NN 1935 WBA - 1. NLP NN 2. NLP 3. 4. 5.
  20. 20. 2035 WBA - NN NN ▼ ▼ ▼ 100 ▶︎▶︎
  21. 21. 2135 WBA - ▼ ( ) ▼ one-hot ( ) ▶︎▶︎ ( ) NN NN
  22. 22. 2235 WBA - N-gram (Embedding) NN RNN, CNNSVM
  23. 23. - 2335 WBA - MeCab, Janome JUMAN++ : : / / / / / / / / / / / / / / / / / / / / / / / / / / /
  24. 24. 2435 WBA - ▼ Twitter UGC ▼ RNN [ + DEIM2018] - NN
  25. 25. 2535 WBA - ▼ 20 (MT) ▼ , n-gram ) - NN
  26. 26. - NN 2635 WBA - ▼ ▼ 1/10
  27. 27. 2735 WBA - N-gram (Embedding) NN RNN, CNNSVM
  28. 28. - one-hot 2835 WBA -
  29. 29. 2935 WBA - - ▼ ▼ cf. Sense Embeddings
  30. 30. NN 3035 WBA - arxiv.org/pdf/1605.07725.pdf ADVERSARIAL TRAINING METHODS FOR SEMI-SUPERVISED TEXT CLASSIFICATION NLP Embedding IMDB Embedding
  31. 31. 3135 WBA - arxiv.org/pdf/1804.08166.pdf Embedding Word Embedding Perturbation for Sentence Classification NLP NN
  32. 32. 3235 WBA - 1. NLP NN 2. NLP 3. 4. 5. NN
  33. 33. 3335 WBA - NN https://www.rinna.jp/
  34. 34. 3435 WBA - Ledge.ai - ledge.ai/chatbot_market_size/
  35. 35. 3535 WBA - ▶︎ ▶︎▶︎ Human-like BLEU, ROUGE, METEOR
  36. 36. 3635 WBA - - BLEU ROUGE (MT) precision MT recall , N-gram
  37. 37. 3735 WBA - www.anlp.jp/proceedings/annual_meeting/2012/pdf_dir/E2-8.pdf N-gram ( ) -
  38. 38. 3835 WBA - How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation arxiv.org/abs/1603.08023 - BLEU Embedding Based
  39. 39. 3935 WBA - Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses arxiv.org/abs/1708.07149 - ADEM RNN hierarchicalRNN[El Hihi and Bengio, 1995;Sordoni+ 2015] [shang+, 2016] Human-like
  40. 40. 4035 WBA - - Human-like
  41. 41. 1. NLP NN 2. NLP 3. 4. 5. NN 4135 WBA -
  42. 42. NLP NN NN NN 4235 WBA -
  43. 43. 4335 WBA - datascience.stackexchange.com/questions/13138/what-is-the- difference-between-word-based-and-char-based-text-generation-rnns What is the difference between word-based and char-based text generation RNNs? Neural Machine Translation of Rare Words with Subword Units arxiv.org/abs/1508.07909 NMT

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