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Coarse-to-Fine Question Answering for Long Documents
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Eunsol Choi, Daniel Hewlett, Jakob Uszkoreit, Illia Polosukhin, Alexandre Lacoste, Jonathan Berant. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017.
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Some work optimizes the retrieval module to aid in a specific, downstream task such as question answering, using search, reinforcement learning, or a latent variable approach as in our work.
“Some work optimizes the retrieval module to aid in a specific, downstream task such as question answering, using search [46], reinforcement learning [6, 63, 62], or a latent variable approach [31, 20] as in our work.”
From Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (2020) · cites this paper as [6]
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“Unlike other recently released large-scale datasets (Rajpurkar et al., 2016; Trischler et al., 2016a), WIKIREADING does not annotate answer spans, making sentence selection more challenging.”
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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.11401 · Cited by 3,101
Doan, Y. T.-T., Ho, M.-T., Nguyen, H.-K., & Han, H.-D. (2021) · Journal of Applied Phycology · doi:10.1007/s10811-021-02488-y · Cited by 20
How Context Affects Language Models' Factual Predictions
Fabio Petroni, P. L. (2020) · Automated Knowledge Base Construction (AKBC) · doi:10.24432/c5201w · Cited by 4
“Works in this areas mostly focus on enhancing retrieval quality [Choi et al., 2017, Wang et al., 2018b,c, Lin et al., 2018, Min et al., 2018, Lee et al., 2018, 2019, Das et al., 2019, Xiong et al., 2019], improving answer aggregation [Clark and Gardner, 2018, Wang et al., 2018c, Lee et al., 2018,…”
Background
Metadata
- Authors
- Choi, E., Hewlett, D., Uszkoreit, J., Polosukhin, I., Lacoste, A., & Berant, J.
- Affiliations
- Tel Aviv University (IL)
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 209-220
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2741263286
- Cited by
- 177
- References
- 6
- Citations by year
- 2026: 4 · 2024: 12 · 2023: 15 · 2022: 14 · 2021: 15 · 2020: 57 · 2019: 36 · 2018: 22 · 2017: 2
- FWCI
- 12.88
- 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)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- long documents, question answering
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