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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
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) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
On the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., & Bengio, Y. (2012) · arXiv · doi:10.48550/arxiv.1211.5063 · Cited by 4,310
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
Seo, M. J., Kembhavi, A., Farhadi, A., & Hajishirzi, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01603 · Cited by 1,698
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
Vector-based Models of Semantic Composition
Mitchell, J., & Lapata, M. (2008) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 680
A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task
Chen, D., Bolton, J., & Manning, C. D. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1223 · Cited by 494
The Goldilocks Principle: Reading Children's Books with Explicit Memory RepresentationsPreprint
Hill, F., Bordes, A., Chopra, S., & Weston, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.02301 · Cited by 478
Attention-over-Attention Neural Networks for Reading Comprehension
Cui, Y., Chen, Z., Wei, S., Wang, S., Liu, T., & Hu, G. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1055 · Cited by 382
Text Understanding with the Attention Sum Reader Network
Kadlec, R., Schmid, M., Bajgar, O., & Kleindienst, J. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1086 · Cited by 299
Shen, Y., Huang, P.-S., Gao, J., & Chen, W. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098177 · Cited by 276
Multi-Task Cross-Lingual Sequence Tagging from ScratchPreprint
Yang, Z., Salakhutdinov, R., & Cohen, W. W. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06270 · Cited by 214
Self-citationTweet2Vec: Character-Based Distributed Representations for Social Media
Dhingra, B., Zhou, Z., Fitzpatrick, D., Muehl, M., & Cohen, W. W. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-2044 · Cited by 188
Self-citationWho did What: A Large-Scale Person-Centered Cloze Dataset
Onishi, T., Wang, H., Bansal, M., Gimpel, K., & McAllester, D. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1241 · Cited by 122
On Multiplicative Integration with Recurrent Neural NetworksPreprint
Wu, Y., Zhang, S., Zhang, Y., Bengio, Y., & Salakhutdinov, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.06630 · Cited by 113
Self-citationDataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question AnsweringPreprint
Li, P., Li, W., He, Z., Wang, X., Cao, Y. S., Zhou, J., & Xu, W. H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.06275 · Cited by 103
The LAMBADA dataset: Word prediction requiring a broad discourse context
Paperno, D., Kruszewski, G., Lazaridou, A., Pham, N. Q., Bernardi, R., Pezzelle, S., Baroni, M., Boleda, G., & Fernandez, R. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1144 · Cited by 87
Natural Language Comprehension with the EpiReader
Trischler, A., Ye, Z., Yuan, X., Bachman, P., Sordoni, A., & Suleman, K. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1013 · Cited by 81
Embracing data abundance: BookTest Dataset for Reading ComprehensionPreprint
Bajgar, O., Kadlec, R., & Kleindienst, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.00956 · Cited by 52
A Multiplicative Model for Learning Distributed Text-Based Attribute RepresentationsPreprint
Kiros, R., Zemel, R. S., & Salakhutdinov, R. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.2710 · Cited by 37
Self-citationDynamic Entity Representation with Max-pooling Improves Machine Reading
Kobayashi, S., Tian, R., Okazaki, N., & Inui, K. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1099 · Cited by 35
Learning Multi-Relational Semantics Using Neural-Embedding ModelsPreprint
Yang, B., Yih, W.-T., He, X., Gao, J., & Deng, L. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.4072 · Cited by 23
Broad Context Language Modeling as Reading Comprehension
Chu, Z., Wang, H., Gimpel, K., & McAllester, D. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-2009 · Cited by 21
Reasoning with Memory Augmented Neural Networks for Language ComprehensionPreprint
Munkhdalai, T., & Yu, H. Q. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.06454 · Cited by 20
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“We refer readers to (Dhingra et al., 2016) for more details.”
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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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