The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Optimizing Statistical Machine Translation for Text Simplification
Open accessAbstract
Most recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a manually simplified parallel corpus. These methods are limited by the quality and quantity of manually simplified corpora, which are expensive to build. In this paper, we conduct an in-depth adaptation of statistical machine translation to perform text simplification, taking advantage of large-scale paraphrases learned from bilingual texts and a small amount of manual simplifications with multiple references. Our work is the first to design automatic metrics that are effective for tuning and evaluating simplification systems, which will facilitate iterative development for this task.
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Minimum error rate training in statistical machine translation
Och, F. J. (2003) · Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - ACL '03 · doi:10.3115/1075096.1075117 · Cited by 2,725
A Neural Attention Model for Abstractive Sentence Summarization
Rush, A. M., Chopra, S., & Weston, J. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1044 · Cited by 2,656
Hierarchical Phrase-Based Translation
Chiang, D. (2007) · Computational Linguistics · doi:10.1162/coli.2007.33.2.201 · Cited by 1,144
Collecting Highly Parallel Data for Paraphrase Evaluation
Chen, D. J., & Dolan, W. B. (2011) · Cited by 898
Paraphrasing with bilingual parallel corpora
Bannard, C., & Callison-Burch, C. (2005) · Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05 · doi:10.3115/1219840.1219914 · Cited by 533
Self-citationSummarization beyond sentence extraction: A probabilistic approach to sentence compression
Knight, K., & Marcu, D. (2002) · Artificial Intelligence · doi:10.1016/s0004-3702(02)00222-9 · Cited by 482
Problems in Current Text Simplification Research: New Data Can Help
Xu, W., Callison-Burch, C., & Napoles, C. (2015) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00139 · Cited by 369
Self-citationA Monolingual Tree-based Translation Model for Sentence Simplification
Zhu, Z., Darmstadt, T. U., Bernhard, D., & Gurevych, I. (2010) · TUbilio (Technical University of Darmstadt) · Cited by 334
Syntax augmented machine translation via chart parsing
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Cited by
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
Rothe, S., Narayan, S., & Severyn, A. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00313 · Cited by 391
Sentence Simplification with Deep Reinforcement Learning
Zhang, X., & Lapata, M. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1062 · Cited by 331
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Aremu, A., Bosselut, A., Chandu, K. R., Clinciu, M.-A., Das, D., Dhole, K., et al. (2021) · Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) · doi:10.18653/v1/2021.gem-1.10 · Cited by 159
Self-citationHandling Divergent Reference Texts when Evaluating Table-to-Text Generation
Dhingra, B., Faruqui, M., Parikh, A., Chang, M.-W., Das, D., & Cohen, W. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1483 · Cited by 150
Natural Language Generation Using Deep Learning to Support MOOC Learners
Li, C., & Xing, W. (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-020-00235-x · Cited by 133
Metadata
- Authors
- Xu, W., Napoles, C., Pavlick, E., Chen, Q., & Callison-Burch, C.
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2016-12
- Volume
- 4
- Pages
- 401-415
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W2534253848
- Cited by
- 521
- References
- 48
- FWCI
- 28.49
- Type
- journal article
- Language
- en
- Open access
- diamond
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