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Rethinking Automatic Evaluation in Sentence Simplification
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In: https://hal.inria.fr/hal-03199901 ; 2021 (2021)
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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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CamemBERT: a Tasty French Language Model
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In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02889805 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, United States. ⟨10.18653/v1/2020.acl-main.645⟩ (2020)
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| 9 |
French Contextualized Word-Embeddings with a sip of CaBeRnet: a New French Balanced Reference Corpus
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In: CMLC-8 - 8th Workshop on the Challenges in the Management of Large Corpora ; https://hal.inria.fr/hal-02678358 ; CMLC-8 - 8th Workshop on the Challenges in the Management of Large Corpora, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/media/proceedings/Workshops/Books/CMLC-8book.pdf (2020)
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MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases ...
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Abstract:
Progress in sentence simplification has been hindered by a lack of labeled parallel simplification data, particularly in languages other than English. We introduce MUSS, a Multilingual Unsupervised Sentence Simplification system that does not require labeled simplification data. MUSS uses a novel approach to sentence simplification that trains strong models using sentence-level paraphrase data instead of proper simplification data. These models leverage unsupervised pretraining and controllable generation mechanisms to flexibly adjust attributes such as length and lexical complexity at inference time. We further present a method to mine such paraphrase data in any language from Common Crawl using semantic sentence embeddings, thus removing the need for labeled data. We evaluate our approach on English, French, and Spanish simplification benchmarks and closely match or outperform the previous best supervised results, despite not using any labeled simplification data. We push the state of the art further by ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://dx.doi.org/10.48550/arxiv.2005.00352
https://arxiv.org/abs/2005.00352
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| 13 |
Controllable Sentence Simplification
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In: https://hal.inria.fr/hal-02445874 ; 2019 (2019)
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| 14 |
CamemBERT: a Tasty French Language Model
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In: https://hal.inria.fr/hal-02445946 ; 2019 (2019)
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| 15 |
Challenges of language change and variation: towards an extended treebank of Medieval French
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In: TLT 2019 - 18th International Workshop on Treebanks and Linguistic Theories ; https://hal.inria.fr/hal-02272560 ; TLT 2019 - 18th International Workshop on Treebanks and Linguistic Theories, Aug 2019, Paris, France (2019)
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| 16 |
Syntactic Parsing versus MWEs: What can fMRI signal tell us
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In: PARSEME-FR 2019 consortium meeting ; https://hal.inria.fr/hal-02272288 ; PARSEME-FR 2019 consortium meeting, Jun 2019, Blois, France ; https://parsemefr.lis-lab.fr/doku.php?id=meeting-20190613 (2019)
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| 17 |
Annotation tools for syntax
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In: Rhapsodie: A Prosodic and Syntactic Treebank for Spoken French ; https://hal.inria.fr/hal-02450311 ; Rhapsodie: A Prosodic and Syntactic Treebank for Spoken French, John Benjamins, 2019, ⟨10.1075/scl.89.08ger⟩ (2019)
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