The DOI matches its Crossref record; 29 of the 33 references it cites were found in a registry or an index.
Gated-Attention Readers for Text Comprehension
Open accessAbstract
In this paper we study the problem of answering cloze-style questions over documents.Our model, the Gated-Attention (GA) Reader 1 , integrates a multi-hop architecture with a novel attention mechanism, which is based on multiplicative interactions between the query embedding and the intermediate states of a recurrent neural network document reader.This enables the reader to build query-specific representations of tokens in the document for accurate answer selection.The GA Reader obtains state-of-the-art results on three benchmarks for this task-the CNN & Daily Mail news stories and the Who Did What dataset.The effectiveness of multiplicative interaction is demonstrated by an ablation study, and by comparing to alternative compositional operators for implementing the gated-attention.
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“The integration of learnable forgetting gates further enhances adaptive memory management by dynamically modulating the retention strength of stored representations based on their predicted utility for future tasks33.”
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Cites
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On the difficulty of training recurrent neural networks
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Recurrent Models of Visual AttentionPreprint
Mnih, V., Heess, N., Graves, A., & Kavukcuoglu, K. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.6247 · Cited by 3,393
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317
Teaching Machines to Read and ComprehendPreprint
Hermann, K. M., Kočiský, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., & Blunsom, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03340 · Cited by 2,855
Bidirectional Attention Flow for Machine ComprehensionPreprint
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Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Kumar, A., Irsoy, O., Ondruska, P., Iyyer, M., Bradbury, J., Gulrajani, I., Zhong, V., Paulus, R., & Socher, R. (2015) · arXiv · doi:10.48550/arxiv.1506.07285 · Cited by 1,020
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Lai, G., Xie, Q., Liu, H., Yang, Y., & Hovy, E. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1082 · Cited by 1,044
“We refer readers to (Dhingra et al., 2016) for more details.”
BackgroundInfluentialSelf-citationGated Self-Matching Networks for Reading Comprehension and Question Answering
Wang, W., Yang, N., Wei, F., Chang, B., & Zhou, M. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1018 · Cited by 674
Attention in Natural Language Processing
Galassi, A., Lippi, M., & Torroni, P. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3019893 · Cited by 726
Differentiable Learning of Logical Rules for Knowledge Base ReasoningPreprint
Yang, F., Yang, Z., & Cohen, W. W. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.08367 · Cited by 434
Self-citationMultifaceted protein–protein interaction prediction based on Siamese residual RCNN
Chen, M., Ju, C. J. ‐., Zhou, G., Chen, X., Zhang, T., Chang, K.-W., Zaniolo, C., & Wang, W. W. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz328 · Cited by 388
Metadata
- Authors
- Dhingra, B., Liu, H., Yang, Z., Cohen, W., & Salakhutdinov, R.
- Affiliations
- Microsoft (United States) (US)
- Carnegie Mellon University (US)
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 1832-1846
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2415755012
- Cited by
- 366
- References
- 33
- Citations by year
- 2026: 4 · 2025: 13 · 2024: 18 · 2023: 17 · 2022: 30 · 2021: 52 · 2020: 56 · 2019: 67 · 2018: 68 · 2017: 34
- FWCI
- 28.06
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1414030)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- text comprehension, multi-hop question answering, attention mechanism, query embedding, answer selection, multiplicative interaction
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