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

1. NN
135 WBA -

2. Koki Yasuda(@himanandayonaxa)
: (B4)
: , ,
: NLP
: nltk
235 WBA -

3. NN
▼ github
github.com/yasudadesu/nlp_analayze
335 WBA -

4. 1. NLP NN
2. NLP
3.
4.
5.
NN
435 WBA -

5. NN
535 WBA -
1. NLP NN
2. NLP
3.
4.
5.

6. 635 WBA -
NLP NN

7. NLP
ACL
2011-2018
www.aclweb.org/
EMNLP
2011-2017
emnlp2017.net/
NAACL HLT
2013, 15, 16, 18
naacl2018.org/
735 WBA -
▼

8. ACL (Association for Computational Linguistics)
year_w …
year_r … accepted paper
835 WBA -

9. 935 WBA -
year_w …
year_r … accepted paper
ACL (Association for Computational Linguistics)

10. EMNLP (Empirical Methods in Natural Language Processing)
1035 WBA -

11. NAACL HLT (North American Chapter of the Association for Computational Linguistics:
Human Language Technologies)
1135 WBA -

12. 1235 WBA -
NLP NN
’neural’
’model’

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. 1. NLP NN
2. NLP
3.
4.
5.
NN
1435 WBA -

15. 1535 WBA -
Slideshare -

16. 1635 WBA -
Slideshare -

17. 1735 WBA -

18. 1835 WBA -
N-gram
(Embedding)
RNN, CNNSVM
NN

19. NN
1935 WBA -
1. NLP NN
2. NLP
3.
4.
5.

20. 2035 WBA -
NN
NN
▼
▼
▼ 100
▶︎▶︎

21. 2135 WBA -
▼ ( )
▼ one-hot ( )
▶︎▶︎ ( )
NN
NN

22. 2235 WBA -
N-gram
(Embedding)
NN
RNN, CNNSVM

23. -
2335 WBA -
MeCab, Janome
JUMAN++
:
:
/ / / / / / / / / /
/ / / / / /
/ / / / / / / /
/ / /

24. 2435 WBA -
▼
Twitter UGC
▼
RNN
[ + DEIM2018]
- NN

25. 2535 WBA -
▼ 20
(MT)
▼ ,
n-gram
)
- NN

26. - NN
2635 WBA -
▼
▼ 1/10

27. 2735 WBA -
N-gram
(Embedding)
NN
RNN, CNNSVM

28. - one-hot
2835 WBA -

29. 2935 WBA -
-
▼
▼
cf. Sense Embeddings

30. NN
3035 WBA -
arxiv.org/pdf/1605.07725.pdf
ADVERSARIAL
TRAINING METHODS FOR
SEMI-SUPERVISED TEXT CLASSIFICATION
NLP
Embedding
IMDB Embedding

31. 3135 WBA -
arxiv.org/pdf/1804.08166.pdf
Embedding
Word Embedding Perturbation
for Sentence Classification
NLP
NN

32. 3235 WBA -
1. NLP NN
2. NLP
3.
4.
5.
NN

33. 3335 WBA -
NN
https://www.rinna.jp/

34. 3435 WBA -
Ledge.ai -
ledge.ai/chatbot_market_size/

35. 3535 WBA -
▶︎
▶︎▶︎ Human-like
BLEU, ROUGE, METEOR

36. 3635 WBA -
-
BLEU ROUGE
(MT)
precision
MT
recall
, N-gram

37. 3735 WBA -
www.anlp.jp/proceedings/annual_meeting/2012/pdf_dir/E2-8.pdf
N-gram
( )
-

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. 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. 4035 WBA -
-
Human-like

41. 1. NLP NN
2. NLP
3.
4.
5.
NN
4135 WBA -

42. NLP
NN
NN
NN
4235 WBA -

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