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Deep Sequoia corpus - PARSEME-FR corpus - FrSemCor
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2
Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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3
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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4
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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5
Controllable Sentence Simplification
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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6
CamemBERT: a Tasty French Language Model
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)
Abstract: International audience ; Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the con-catenation of data in multiple languages. This makes practical use of such models-in all languages except English-very limited. In this paper, we investigate the feasibility of training monolingual Transformer-based language models for other languages, taking French as an example and evaluating our language models on part-of-speech tagging, dependency parsing, named entity recognition and natural language inference tasks. We show that the use of web crawled data is preferable to the use of Wikipedia data. More surprisingly, we show that a relatively small web crawled dataset (4GB) leads to results that are as good as those obtained using larger datasets (130+GB). Our best performing model CamemBERT reaches or improves the state of the art in all four downstream tasks.
Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
URL: https://hal.inria.fr/hal-02889805/file/ACL_2020___CamemBERT__a_Tasty_French_Language_Model-6.pdf
https://hal.inria.fr/hal-02889805
https://hal.inria.fr/hal-02889805/document
https://doi.org/10.18653/v1/2020.acl-main.645
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7
French Contextualized Word-Embeddings with a sip of CaBeRnet: a New French Balanced Reference Corpus
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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8
Universal Dependencies 2.7
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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9
Universal Dependencies 2.6
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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10
Controllable Sentence Simplification
In: https://hal.inria.fr/hal-02445874 ; 2019 (2019)
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11
CamemBERT: a Tasty French Language Model
In: https://hal.inria.fr/hal-02445946 ; 2019 (2019)
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12
Challenges of language change and variation: towards an extended treebank of Medieval French
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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13
Syntactic Parsing versus MWEs: What can fMRI signal tell us
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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14
Annotation tools for syntax
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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15
Universal Dependencies 2.5
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019
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16
Universal Dependencies 2.4
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019
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17
Reference-less Quality Estimation of Text Simplification Systems
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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18
ELMoLex: Connecting ELMo and Lexicon features for Dependency Parsing
In: CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies ; https://hal.inria.fr/hal-01959045 ; CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, Oct 2018, Brussels, Belgium. ⟨10.18653/v1/K18-2023⟩ (2018)
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19
Cheating a Parser to Death: Data-driven Cross-Treebank Annotation Transfer
In: Eleventh International Conference on Language Resources and Evaluation (LREC 2018) ; https://hal.inria.fr/hal-01798801 ; Eleventh International Conference on Language Resources and Evaluation (LREC 2018), May 2018, Miyazaki, Japan (2018)
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20
ANCOR-AS: Enriching the ANCOR Corpus with Syntactic Annotations
In: LREC 2018 - 11th edition of the Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-01744572 ; LREC 2018 - 11th edition of the Language Resources and Evaluation Conference, May 2018, Miyazaki, Japan ; http://lrec2018.lrec-conf.org/en/ (2018)
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