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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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Abstract:
Progress in Sentence Simplification has been hindered by the lack of supervised data, particularly in languages other than English. Previous work has aligned sentences from original and simplified corpora such as English Wikipedia and Simple English Wikipedia, but this limits corpus size, domain, and language. In this work, we propose using unsupervised mining techniques to automatically create training corpora for simplification in multiple languages from raw Common Crawl web data. When coupled with a controllable generation mechanism that can flexibly adjust attributes such as length and lexical complexity, these mined paraphrase corpora can be used to train simplification systems in any language. We further incorporate multilingual unsupervised pretraining methods to create even stronger models and show that by training on mined data rather than supervised corpora, we outperform the previous best results. We evaluate our approach on English, French, and Spanish simplification benchmarks and reach state-of-the-art performance with a totally unsupervised approach. We will release our models and code to mine the data in any language included in Common Crawl.
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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-03109299
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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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Augmenting Transformers with KNN-Based Composite Memory for Dialog
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In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02999678 ; Transactions of the Association for Computational Linguistics, The MIT Press, In press, ⟨10.1162/tacl_a_00356⟩ ; https://transacl.org/index.php/tacl (2020)
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MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases ...
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ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations ...
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ASSET: A dataset for tuning and evaluation of sentence simplification models with multiple rewriting transformations
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Controllable Sentence Simplification
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In: https://hal.inria.fr/hal-02445874 ; 2019 (2019)
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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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Extracting biomedical events from pairs of text entities
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In: ISSN: 1471-2105 ; BMC Bioinformatics ; https://hal.archives-ouvertes.fr/hal-01313324 ; BMC Bioinformatics, BioMed Central, 2015, 16 (Suppl 10), pp.S8. ⟨10.1186/1471-2105-16-S10-S8⟩ ; http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-16-S10-S8 (2015)
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Open Question Answering with Weakly Supervised Embedding Models
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In: European Conference (ECML PKDD 2014) ; https://hal.archives-ouvertes.fr/hal-01344007 ; European Conference (ECML PKDD 2014), Sep 2014, nancy, France. pp.165-180, ⟨10.1007/978-3-662-44848-9_11⟩ (2014)
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Fast recursive multi-class classification of pairs of text entities for biomedical event extraction
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In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01060830 ; Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2014, Gothenburg, Sweden. pp.692--701 (2014)
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Open Question Answering with Weakly Supervised Embedding Models ...
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Towards Understanding Situated Natural Language
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In: 13th International Conference on Artificial Intelligence and Statistics ; https://hal.archives-ouvertes.fr/hal-00750937 ; 13th International Conference on Artificial Intelligence and Statistics, May 2010, Chia Laguna Resort, Sardinia, Italy. pp.65-72 (2010)
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Extracting biomedical events from pairs of text entities
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In: ISSN: 1471-2105 ; BMC Bioinformatics ; https://hal.archives-ouvertes.fr/hal-01278279 ; BMC Bioinformatics, BioMed Central, 2005, 16 (Suppl 10), pp.S8. ⟨10.1186/1471-2105-16-S10-S8⟩ ; http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-16-S10-S8 (2005)
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