The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Gated Self-Matching Networks for Reading Comprehension and Question Answering
Open accessAbstract
In this paper, we present the gated selfmatching networks for reading comprehension style question answering, which aims to answer questions from a given passage.We first match the question and passage with gated attention-based recurrent networks to obtain the question-aware passage representation.Then we propose a self-matching attention mechanism to refine the representation by matching the passage against itself, which effectively encodes information from the whole passage.We finally employ the pointer networks to locate the positions of answers from the passages.We conduct extensive experiments on the SQuAD dataset.The single model achieves 71.3% on the evaluation metrics of exact match on the hidden test set, while the ensemble model further boosts the results to 75.9%.At the time of submission of the paper, our model holds the first place on the SQuAD leaderboard for both single and ensemble model.
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SQuAD: 100,000+ Questions for Machine Comprehension of Text
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Recurrent neural network based language model
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Cited by
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Know What You Don't Know: Unanswerable Questions for SQuAD
Rajpurkar, P., Jia, R., & Liang, P. (2018) · arXiv · doi:10.48550/arxiv.1806.03822 · Cited by 2,242
Know What You Don’t Know: Unanswerable Questions for SQuAD
Rajpurkar, P., Jia, R., & Liang, P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p18-2124 · Cited by 2,240
A Survey of the Usages of Deep Learning for Natural Language Processing
Otter, D. W., Medina, J. R., & Kalita, J. K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2979670 · Cited by 1,836
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., & Manning, C. D. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1259 · Cited by 1,804
Metadata
- Authors
- Wang, W., Yang, N., Wei, F., Chang, B., & Zhou, M.
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 189-198
- OpenAlex
- W2740747242
- Cited by
- 666
- References
- 36
- FWCI
- 55.64
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Wang, W., Yang, N., Wei, F., Chang, B., & Zhou, M. (2017). Gated Self-Matching Networks for Reading Comprehension and Question Answering. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p17-1018
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