Paper Reading: Papers in Frontiers of NLP 2018 collection
1.Papers collections
Note: the original name of the paper will be appended soonly!
Index | Paper | Year | Brief Intro | Note |
---|---|---|---|---|
1. | [Collobert & Weston, ICML ’08] | 2008 | Multi-task learning. | MTL: Win Test-of-time-award at ICML 2018 |
2. | [Pennington et al., EMNLP ’14; Levy et al., NIPS ’14] | 2014 | Generate embeeidng by matrix factorization | New method of embedding |
3. | [Levy et al., TACL ’15] | 2015 | Classic methods (eg. PMI and SVD) for embedding generation | New method of embedding |
4. | [Le & Mikolov, ICML ’14; Kiros et al., NIPS ’15] | 2016 | Skip-gram for sentence representation | Skip-gram |
5. | [Grover & Leskovec, KDD ’16] | 2016 | Skip-gram for Nueral Network modelling | Skip-gram |
6. | [Luong et al., ’15] | 2015 | Difference embedding projection aids trasfer learning | Embedding projection |
7. | [Hochreiter & Schmidhuber, NeuComp ’97] | 1997 | The original paper for LSTM | LSTM |
8. | [Kalchbrenner et al., ’17] | 2017 | Dilated CNN | CNN: To enable wider receptive field |
9. | [Wang et al., ACL ’16] | 2016 | Stacked LSTM and CNN | Stacked model |
10. | [Bradbury et al., ICLR ’17] | 2017 | Use convolution to speed up LSTM | CNN&LSTM combination |
11. | [Tai et al., ACL ’15] | 2015 | Extend Recursive nueral netword to LSTM | Recursive neural network put forward |
12. | [Bastings et al., EMNLP ’17] | 2017 | graph convolutional neural network | Cnn over graph(trees) |
13. | [Levy and Goldberg, ACL ’14] | 2014 | word embeddings generated form dependencies | Embedding generation |
14. | [Wu et al., ’16] | 2016 | Deep LSTM | New seq2seq model |
15. | [Kalchbrenner et al., arXiv ’16; Gehring et al., arXiv ’17] | 2017 | Convolutional encoders | New seq2seq model |
16. | [Vaswani et al., NIPS ’17] | 2017 | Transformer: pure attention architecture | New seq2seq model |
17. | [Chen et al., ACL ’18] | 2018 | combination of LSTM and Transformer | New seq2seq model |
18. | [Vinyals et al., NIPS ’16] | 2016 | Attention in one-shot learning | Attention & one-shot |
19.0 | [Graves et al., arXiv ’14] | 2014 | Neural Turing Machine | Memory Network |
19.1 | [Weston et al., ICLR ’15] | 2015 | Memory Network | Memory Network |
19.2 | [Sukhbaatar et al., NIPS ’15] | 2015 | End-to-end Memory Networks | Memory Network |
19.3 | Dynamic Memory Networks [Kumar et al., ICML ’16] | 2016 | Dynamic Memory Networks | Memory Network |
19.4 | [Graves et al., Nature ’16] | 2016 | Neural Differentiable Computer | Memory Network |
19.5 | [Henaff et al., ICLR ’17] | 2017 | Recurrent Entity Network | Memory Network |
20. | [Peters et al., NAACL ’18],之前看过一篇稍后补上 | 2018 | Language model embedding used as feature | Language model |
21. | [Howard & Ruder, ACL ’18] | 2018 | Language model fine tuned on task data | Language model |
22. | [Jia & Liang, EMNLP ’17] | 2017 | Adversarial examples | Adversarial |
23. | [Miyato et al., ICLR ’17; Yasunaga et al., NAACL’18] | 2018 | Adversarial training | Form of regularization |
24. | [Ganin et al., JMLR ’16; Kim et al., ACL ’17] | 2017 | Domain adversarial loss | Form of regularization |
25. | [Semeniuta et al., ’18] | 2018 | GANs’ application in NLG | GAN for NLP |
26. | [Paulus et al., ICLR ’18] | 2018 | RL for summarization | RL with ROUGE loss |
27. | [Ranzato et al., ICLR ’16] | 2016 | RL for Machine Translation | RL with BLUE loss |
28. | [Conneau et al., ICLR’18] | 2018 | word translation without parallel data | Low-resource scenarios |
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