The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Sentence Simplification with Deep Reinforcement Learning
Open accessAbstract
Sentence simplification aims to make sentences easier to read and understand.Most recent approaches draw on insights from machine translation to learn simplification rewrites from monolingual corpora of complex and simple sentences.We address the simplification problem with an encoder-decoder model coupled with a deep reinforcement learning framework.Our model, which we call DRESS (as shorthand for Deep REinforcement Sentence Simplification), explores the space of possible simplifications while learning to optimize a reward function that encourages outputs which are simple, fluent, and preserve the meaning of the input.Experiments on three datasets demonstrate that our model outperforms competitive simplification systems. 1
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Human-level control through deep reinforcement learning
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A Gentle Introduction to Reinforcement Learning and its Application in Different Fields
Naeem, M., Rizvi, S. T. H., & Coronato, A. (2020) · IEEE Access · doi:10.1109/access.2020.3038605 · Cited by 257
Paragraph-level Simplification of Medical Texts
Devaraj, A., Marshall, I., Wallace, B., & Li, J. J. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.395 · Cited by 79
Evaluating Factuality in Text Simplification
Devaraj, A., Sheffield, W., Wallace, B., & Li, J. J. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.506 · Cited by 40
Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System
Liu, Y., Zhang, C., Yan, X., Chang, Y., & Yu, P. S. (2019) · Proceedings of the 28th ACM International Conference on Information and Knowledge Management · doi:10.1145/3357384.3358046 · Cited by 15
HALoGEN: Fantastic LLM Hallucinations and Where to Find Them
Ravichander, A., Ghela, S., Wadden, D., & Choi, Y. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2025.acl-long.71 · Cited by 13
Metadata
- Authors
- Zhang, X., & Lapata, M.
- Venue
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Published
- 2017
- Pages
- 584-594
- OpenAlex
- W2605243085
- Cited by
- 331
- References
- 58
- FWCI
- 17.15
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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