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Multilingual Unsupervised Sentence Simplification
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In: https://hal.inria.fr/hal-03109299 ; 2021 (2021)
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Controllable Sentence Simplification
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In: LREC 2020 - 12th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-02678214 ; LREC 2020 - 12th Language Resources and Evaluation Conference, May 2020, Marseille, France ; http://www.lrec-conf.org/proceedings/lrec2020/index.html (2020)
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ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations
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In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02889823 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, United States (2020)
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Controllable Sentence Simplification
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In: https://hal.inria.fr/hal-02445874 ; 2019 (2019)
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Abstract:
Code and models: https://github.com/facebookresearch/access ; Text simplification aims at making a text easier to read and understand by simplifying grammar and structure while keeping the underlying information identical. It is often considered an all-purpose generic task where the same simplification is suitable for all; however multiple audiences can benefit from simplified text in different ways. We adapt a discrete parametrization mechanism that provides explicit control on simplification systems based on Sequence-to-Sequence models. As a result, users can condition the simplifications returned by a model on parameters such as length, amount of paraphrasing, lexical complexity and syntactic complexity. We also show that carefully chosen values of these parameters allow out-of-the-box Sequence-to-Sequence models to outperform their standard counterparts on simplification benchmarks. Our model, which we call ACCESS (as shorthand for AudienCe-CEntric Sentence Simplification), increases the state of the art to 41.87 SARI on the WikiLarge test set, a +1.42 gain over previously reported scores.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
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URL: https://hal.inria.fr/hal-02445874
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Reference-less Quality Estimation of Text Simplification Systems
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In: 1st Workshop on Automatic Text Adaptation (ATA) ; https://hal.inria.fr/hal-01959054 ; 1st Workshop on Automatic Text Adaptation (ATA), Nov 2018, Tilburg, Netherlands ; https://www.ida.liu.se/~evere22/ATA-18/ (2018)
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Fader Networks: Manipulating Images by Sliding Attributes
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In: 31st Conference on Neural Information Processing Systems (NIPS 2017) ; https://hal.archives-ouvertes.fr/hal-02275215 ; 31st Conference on Neural Information Processing Systems (NIPS 2017), Dec 2017, Long Beach, CA, United States. pp.5969-5978 (2017)
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