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Establishing a New State-of-the-Art for French Named Entity Recognition
In: LREC 2020 - 12th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-02617950 ; LREC 2020 - 12th Language Resources and Evaluation Conference, May 2020, Marseille, France ; http://www.lrec-conf.org (2020)
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OFrLex: A Computational Morphological and Syntactic Lexicon for Old French
In: LREC 2020 - 12th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-02677957 ; LREC 2020 - 12th Language Resources and Evaluation Conference, May 2020, Marseille, France. 3217-3225 (updated version) (2020)
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3
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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4
Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell
In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02889804 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, Canada. ⟨10.18653/v1/2020.acl-main.107⟩ (2020)
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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)
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6
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages
In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02863875 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, United States. ⟨10.18653/v1/2020.acl-main.156⟩ ; https://acl2020.org (2020)
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7
Methodological Aspects of Developing and Managing an Etymological Lexical Resource: Introducing EtymDB 2.0
In: LREC 2020 - 12th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-02678100 ; LREC 2020 - 12th Language Resources and Evaluation Conference, May 2020, Marseille, France (2020)
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8
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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ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations
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)
Abstract: International audience ; In order to simplify a sentence, human editors perform multiple rewriting transformations: they split it into several shorter sentences , paraphrase words (i.e. replacing complex words or phrases by simpler synonyms), reorder components, and/or delete information deemed unnecessary. Despite these varied range of possible text alterations, current models for automatic sentence simplification are evaluated using datasets that are focused on a single transformation, such as lexical paraphrasing or splitting. This makes it impossible to understand the ability of simplification models in more realistic settings. To alleviate this limitation, this paper introduces ASSET, a new dataset for assessing sentence simplification in English. ASSET is a crowdsourced multi-reference corpus where each simplification was produced by executing several rewriting transformations. Through quantitative and qualitative experiments, we show that simplifications in ASSET are better at capturing characteristics of simplicity when compared to other standard evaluation datasets for the task. Furthermore, we motivate the need for developing better methods for automatic evaluation using ASSET, since we show that current popular metrics may not be suitable when multiple simplification transformations are performed.
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-02889823/file/ACL_2020___ASSET__A_Dataset_for_Tuning_and_Evaluation___.pdf
https://hal.inria.fr/hal-02889823
https://hal.inria.fr/hal-02889823/document
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Evaluating the reliability of acoustic speech embeddings
In: INTERSPEECH 2020 - Annual Conference of the International Speech Communication Association ; https://hal.inria.fr/hal-02977539 ; INTERSPEECH 2020 - Annual Conference of the International Speech Communication Association, Oct 2020, Shanghai / Vitrtual, China (2020)
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11
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models
In: https://hal.inria.fr/hal-03109106 ; 2020 (2020)
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12
Comparing Statistical and Neural Models for Learning Sound Correspondences
In: LT4HALA 2020 : First Workshop on Language Technologies for Historical and Ancient Languages ; https://hal.inria.fr/hal-02529929 ; LT4HALA 2020 : First Workshop on Language Technologies for Historical and Ancient Languages, May 2020, Marseille, France (2020)
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13
Universal Dependencies 2.7
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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14
Universal Dependencies 2.6
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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15
Can Multilingual Language Models Transfer to an Unseen Dialect? A Case Study on North African Arabizi ...
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16
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering ...
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17
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages ...
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18
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models ...
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19
MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases ...
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20
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations ...
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