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論文紹介: Mojitalk: generating emotional responses at scale
- 1. MOJITALK: Generating Emotional Responses at Scale ACL2018
- 2. : (B4, ) : , , : NLP 1 , CV : , 3D , NMT, HCI
- 3. 🐠 MOJITALK: Generating Emotional Responses at Scale : ▶︎ ▶︎ ( ) ▶︎ #kawaii_paper_name
- 4. MOJITALK: Generating Emotional Responses at Scale 1.Introduction 2.Conclusion 3.Related Work 4.Dataset 5.Model 6.Experimental Results 7.Conclusion & Future Work
- 5. MOJITALK: Generating Emotional Responses at Scale 1.Introduction 🐶 2.Conclusion 3.Related Work 4.Dataset 5.Model 6.Experimental Results 7.Conclusion & Future Work
- 6. … Abstract 🐥
- 7. Introduction 🐶 : ▶︎ (emotional) : ▶︎ … [Pang et al., 2002; Maas et al., 2011; Socher et al., 2013]
- 8. Introduction 🐶 + ▶︎ [Go et al., 2016], [Li et al., 2017b] ▶︎ [Pang et al., 2002; Maas et al., 2011; Socher et al., 2013] Twitter
- 9. Introduction 🐶 Original tweet( ) Response( )
- 10. Introduction 🐶 ▶︎ IR [Huang et al., 2017] ▶︎ CVAE VAE [Zhao et al., 2017] ▶︎ sentence-to-emoji classifier[Feibo et al., 2017]
- 11. MOJITALK: Generating Emotional Responses at Scale 1.Introduction 2.Conclusion 🐱 3.Related Work 4.Dataset 5.Model 6.Experimental Results 7.Conclusion & Future Work
- 12. Conclusion 🐱
- 13. MOJITALK: Generating Emotional Responses at Scale 1.Introduction 2.Conclusion 3.Related Work 🐭 4.Dataset 5.Model 6.Experimental Results 7.Conclusion & Future Work
- 14. Related Work 🐭 Twitter Emoji2vec [Eisner et al., 2016] ▶︎
- 15. Related Work 🐭 Twitter Emoji2vec [Eisner et al., 2016] ▶︎
- 16. Related Work 🐭 Twitter DeepMoji [Felbo et al., 2017] ▶︎ bi-LSTM https://deepmoji.mit.edu/
- 17. Related Work 🐭 ConditionalVAE [Sohn et al., 2015] ▶︎ ( )
- 18. Related Work 🐭 [Li et al., 2016][Li et al., 2017] ▶︎ Action , State , Policy[pi, qi] pRL(pi+1 |pi, qi)
- 19. MOJITALK: Generating Emotional Responses at Scale 1.Introduction 2.Conclusion 3.Related Work 4.Dataset 🐹 5.Model 6.Experimental Results 7.Conclusion & Future Work
- 20. Dataset 🐹 ▶︎ SNS ▶︎ 64 DeepMoji
- 21. Dataset 🐹 ▶︎ 2017 8 12~14 ▶︎ ▶︎ URL, , ▶︎
- 22. Dataset 🐹 ▶︎ ▶︎
- 24. Dataset 🐹
- 25. MOJITALK: Generating Emotional Responses at Scale 1.Introduction 2.Conclusion 3.Related Work 4.Dataset 5.Model 🐰 6.Experimental Results 7.Conclusion & Future Work
- 26. Model 🐰 seq2seq + Attention [Luong et al., 2015] ▶︎ ▶︎ ▶︎ embedding->Dense
- 27. Model 🐰 CVAE [Sohn et al., 2015] p(x|c) = ∫ p(x|z, c)p(z|c)dz [vo; ve]
- 28. Model 🐰 CVAE p(x|c) = ∫ p(x|z, c)p(z|c)dz ▶︎ cf)VAE BoW loss
- 29. Model 🐰 CVAE recog/prior network Reparameterization trick[Kingma and Welling, 2013] recog net z decoder Prior net
- 30. Model 🐰 CVAE CVAE Attention ( ) ▶︎ VAE Decoder [Bowman et al., 2015] KL loss construction loss ▶︎ KL annealing early stopping [Bowman et al., 2015] bag-of-words loss [Zhao et al., 2017]
- 31. Model 🐰 Reinforced CVAE ▶︎ CVAE policy gradient( ) policy gradient bi-GRU [Felbo et al., 2017] ▶︎ policy training x c CVAE x’ x’ R x emoji clf
- 32. Model 🐰 Reinforced CVAE REINFORCE ▶︎ ▶︎ Reinforce hybrid objective α ∈ [0,1]
- 33. MOJITALK: Generating Emotional Responses at Scale 1.Introduction 2.Conclusion 3.Related Work 4.Dataset 5.Model 6.Experimental Results 🦊 7.Conclusion & Future Work
- 34. Experimental Results 🦊 CVAE seq2seq ▶︎ Perplexity ( ) : www.slideshare.net/hoxo_m/perplexity ▶︎ top5 ▶︎ SotA
- 35. Experimental Results 🦊 ▶︎ seq2seq ▶︎ CVAE ▶︎ 1-gram, 2-gram, 3-gram ▶︎ Reinforced CVAE (?)
- 36. Experimental Results 🦊 ▶︎ top5 CVAE Reinforced CVAE
- 37. Experimental Results 🦊 ▶︎ Amazon Mechanical Turk 100 5 ▶︎
- 38. Experimental Results 🦊 ▶︎ Turing Test ▶︎ 18% 27% ▶︎ (inter-rater reliability)
- 39. Experimental Results 🦊 ▶︎ seq2seq I’m ( ) ▶︎ CVAE ▶︎ Reinforced CVAE
- 40. MOJITALK: Generating Emotional Responses at Scale 1.Introduction 2.Conclusion 3.Related Work 4.Dataset 5.Model 6.Experimental Results 7.Conclusion & Future Work 🐮
- 41. Conclusion & Future Work 🐮
- 42. Thank you for listening 👻