https://github.com/shujian2015/nlp_papers
Recent NLP papers that I found interesting
https://github.com/shujian2015/nlp_papers
Last synced: 3 months ago
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Recent NLP papers that I found interesting
- Host: GitHub
- URL: https://github.com/shujian2015/nlp_papers
- Owner: Shujian2015
- License: mit
- Created: 2018-11-30T16:53:11.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-11-30T17:58:39.000Z (over 6 years ago)
- Last Synced: 2025-02-15T12:50:03.233Z (3 months ago)
- Homepage:
- Size: 3.91 KB
- Stars: 9
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
List some NLP papers I found interesting.
### 2018
* Smith, Leslie N. "[A disciplined approach to neural network hyper-parameters: Part 1--learning rate, batch size, momentum, and weight decay.](https://arxiv.org/abs/1803.09820)" arXiv preprint arXiv:1803.09820 (2018).
* Lample, Guillaume, et al. "[Phrase-Based & Neural Unsupervised Machine Translation.](https://arxiv.org/abs/1804.07755)" arXiv preprint arXiv:1804.07755 (2018).
* Strubell, Emma, et al. "[Linguistically-Informed Self-Attention for Semantic Role Labeling.](https://arxiv.org/abs/1804.08199)" arXiv preprint arXiv:1804.08199 (2018).
* Artetxe, Mikel, et al. "[Uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation.](http://aclweb.org/anthology/K18-1028)" arXiv preprint arXiv:1809.02094 (2018).
* Kiela, Douwe, Changhan Wang, and Kyunghyun Cho. "[Dynamic Meta-Embeddings for Improved Sentence Representations.](https://arxiv.org/abs/1804.07983)" Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.
* Howard, Jeremy, and Sebastian Ruder. "[Universal language model fine-tuning for text classification.](https://arxiv.org/abs/1801.06146)" Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vol. 1. 2018.
* Peters, Matthew E., et al. "[Deep contextualized word representations.](https://arxiv.org/abs/1802.05365)" arXiv preprint arXiv:1802.05365 (2018).
* Devlin, Jacob, et al. "[Bert: Pre-training of deep bidirectional transformers for language understanding.](https://arxiv.org/abs/1810.04805)" arXiv preprint arXiv:1810.04805 (2018).
* Tang, Gongbo, et al. "[Why self-attention? a targeted evaluation of neural machine translation architectures.](https://arxiv.org/abs/1808.08946)" arXiv preprint arXiv:1808.08946 (2018).
* Caccia, Massimo, et al. "[Language GANs Falling Short.](https://arxiv.org/abs/1811.02549)" arXiv preprint arXiv:1811.02549 (2018).
* Edward Collins, et al. "[Evolutionary Data Measures: Understanding the Difficulty of Text Classification Tasks](https://arxiv.org/abs/1811.01910)" arXiv preprint arXiv:1811.01910 (2018).
* Futrell, Richard, and Roger P. Levy. "[Do RNNs learn human-like abstract word order preferences?.](https://arxiv.org/abs/1811.01866)" arXiv preprint arXiv:1811.01866 (2018).
* Gehrmann, Sebastian, Yuntian Deng, and Alexander M. Rush. "[Bottom-up abstractive summarization.](https://arxiv.org/abs/1808.10792)" arXiv preprint arXiv:1808.10792 (2018).
* Jacovi, Alon, Oren Sar Shalom, and Yoav Goldberg. "[Understanding Convolutional Neural Networks for Text Classification.](https://arxiv.org/abs/1809.08037)" arXiv preprint arXiv:1809.08037 (2018).
* Chen, Jianbo, et al. "[Learning to Explain: An Information-Theoretic Perspective on Model Interpretation.](https://arxiv.org/abs/1802.07814)" arXiv preprint arXiv:1802.07814 (2018).
* Cer, Daniel, et al. "[Universal sentence encoder.](https://arxiv.org/abs/1803.11175)" arXiv preprint arXiv:1803.11175 (2018).
## 2019