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Towards Unsupervised Content Disentanglement in Sentence Representations via Syntactic Roles
In: CtrlGen: Controllable Generative Modeling in Language and Vision ; https://hal.inria.fr/hal-03540084 ; CtrlGen: Controllable Generative Modeling in Language and Vision, Jan 2022, virtual, France (2022)
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2
Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?
In: Seventh Workshop on Noisy User-generated Text (W-NUT 2021, colocated with EMNLP 2021) ; https://hal.inria.fr/hal-03527328 ; Seventh Workshop on Noisy User-generated Text (W-NUT 2021, colocated with EMNLP 2021), Jan 2022, punta cana, Dominican Republic ; https://aclanthology.org/2021.wnut-1.47/ (2022)
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3
First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT
In: https://hal.inria.fr/hal-03161685 ; 2021 (2021)
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4
Can Multilingual Language Models Transfer to an Unseen Dialect? A Case Study on North African Arabizi
In: https://hal.inria.fr/hal-03161677 ; 2021 (2021)
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5
First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT
In: EACL 2021 - The 16th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03239087 ; EACL 2021 - The 16th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2021, Kyiv / Virtual, Ukraine ; https://2021.eacl.org/ (2021)
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6
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models
In: NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; https://hal.inria.fr/hal-03251105 ; NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jun 2021, Mexico City, Mexico (2021)
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7
PAGnol: An Extra-Large French Generative Model
In: https://hal.inria.fr/hal-03540159 ; [Research Report] LightON. 2021 (2021)
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8
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
In: https://hal.inria.fr/hal-03109187 ; 2021 (2021)
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9
Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Models
In: W-NUT 2021 - 7th Workshop on Noisy User-generated Text (colocated with EMNLP 2021) ; https://hal.inria.fr/hal-03540174 ; W-NUT 2021 - 7th Workshop on Noisy User-generated Text (colocated with EMNLP 2021), Association for computational linguistics, Nov 2021, Punta Cana, Dominican Republic (2021)
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10
Understanding the Impact of UGC Specificities on Translation Quality
In: W-NUT 2021 - Seventh Workshop on Noisy User-generated Text (colocated with EMNLP 2021) ; https://hal.inria.fr/hal-03540175 ; W-NUT 2021 - Seventh Workshop on Noisy User-generated Text (colocated with EMNLP 2021), association for computational linguistics, Nov 2021, Punta Cana, Dominican Republic (2021)
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11
Challenging the Semi-Supervised VAE Framework for Text Classification
In: Second Workshop on Insights from Negative Results in NLP (colocated with EMNLP) ; https://hal.inria.fr/hal-03540081 ; Second Workshop on Insights from Negative Results in NLP (colocated with EMNLP), Nov 2021, Punta Cana, Dominican Republic ; https://insights-workshop.github.io/2021/ (2021)
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12
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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13
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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14
Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora
In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03161637 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, United States. pp.538-555, ⟨10.18653/v1/2020.acl-main.51⟩ (2020)
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15
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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16
Unsupervised Learning for Handling Code-Mixed Data: A Case Study on POS Tagging of North-African Arabizi Dialect
In: EurNLP - First annual EurNLP ; https://hal.archives-ouvertes.fr/hal-02270527 ; EurNLP - First annual EurNLP, Oct 2019, Londres, United Kingdom (2019)
Abstract: International audience ; Language model pretrained representation are now ubiquitous in Natural Language Processing. In this work, we present some first results in adapting those models to Out-of-Domain textual data. Using Part-of-Speech tagging as our case study, we analyze the ability of BERT to model a complex North-African Dialect (Arabizi). What is Arabizi ? BERT and Arabizi We do our experiments on the released base multilingual version of BERT (Delvin et al. 2018) which was trained on a concatenation of Wikipedia of 104 languages. BERT has never seen any Arabizi. It is visible that Arabizi is related to French in BERT's embedding space Summary • Multilingual-BERT can be used to build a decent Part-of-Speech Tagger with a reasonable amount of annotated data • Unsupervised adaptation improves (+1) performance in downstream POS tagging Research questions • Is BERT able to model Out-of-Domain languages such as Arabizi ? • Can we adapt BERT in an unsupervised way to Arabizi ? Definitions • Dialectal Arabic is a variation of Classic Arabic that varies from one region to another that is spoken orally only. Darija is the one spoken in Maghreb (Algeria, Tunisia, Morocco). • Arabizi is the name given to the transliterated language of dialectal Arabic in Latin script mostly found online. Key Property : High Variability • No spelling, morphological or syntactic fixed norms • Strong influence from foreign languages • Code-Switching French / Darija Unsupervised Fine Tuning of BERT on Arabizi We fine-tune BERT (MLM objective) on the 200k Arabizi sentences Results Collecting and filtering raw Arabizi Data We bootstrap a data set for Arabizi starting from 9000 sentences collected by Cotterell et al. (2014). Using keywords scraping, we collect 1 million UGC sentences comprising French, English and Arabizi. We filter 200k Arabizi sentences out of the raw corpus (94% F1 score) using our language identifier (cf. Figure below). Lexical Normalization We train a clustering lexical normalizer using edit and word2vec distances. This degrades downstream performances in POS tagging. A new Treebank The first bottleneck in analyzing such a dialect is the lack of annotated resources. We developed a CoNLL-U Treebank** that includes Part-of-Speech, dependencies, and the translations of 1500 sentences (originally posted in Facebook, Echorouk newspaper…). Model Accuracy Baseline (udpipe) 73.7 Baseline + Normalization (udpipe) 72.4 BERT + POS tuning 77.3 BERT + POS tuning + Normalization (udpipe) 69.9 BERT + Unsupervised Domain fine tuning+ POS tuning 78.3 Final performance. Accuracy reported on the test set averaged over 5 runs Figure 2 : Validation accuracy while fine tuning BERT on Arabizi data (200k sentence) X1000 iteration Accuracy Masked Language Model French Wikipedia Arabizi vive mca w nchalah had l'3am championi Arabizi long live MCA and I hope that this year we will be champions English
Keyword: [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing
URL: https://hal.archives-ouvertes.fr/hal-02270527/file/poster-EURNLP.pdf
https://hal.archives-ouvertes.fr/hal-02270527/document
https://hal.archives-ouvertes.fr/hal-02270527
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17
CamemBERT: a Tasty French Language Model
In: https://hal.inria.fr/hal-02445946 ; 2019 (2019)
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18
Enhancing BERT for Lexical Normalization
In: The 5th Workshop on Noisy User-generated Text (W-NUT) ; https://hal.inria.fr/hal-02294316 ; The 5th Workshop on Noisy User-generated Text (W-NUT), Nov 2019, Hong Kong, China (2019)
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19
What does BERT learn about the structure of language?
In: ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02131630 ; ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Jul 2019, Florence, Italy (2019)
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20
Contextualized Diachronic Word Representations
In: 1st International Workshop on Computational Approaches to Historical Language Change 2019 (colocated with ACL 2019) ; https://hal.archives-ouvertes.fr/hal-02194763 ; 1st International Workshop on Computational Approaches to Historical Language Change 2019 (colocated with ACL 2019), Aug 2019, Florence, Italy (2019)
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