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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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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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