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Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP
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In: https://hal.inria.fr/hal-03540069 ; 2022 (2022)
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Automatic Normalisation of Early Modern French
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In: https://hal.inria.fr/hal-03540226 ; 2022 (2022)
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From FreEM to D'AlemBERT ; From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early Modern French
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In: Proceedings of the 13th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-03596653 ; Proceedings of the 13th Language Resources and Evaluation Conference, European Language Resources Association, Jun 2022, Marseille, France (2022)
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Towards a Cleaner Document-Oriented Multilingual Crawled Corpus
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In: https://hal.inria.fr/hal-03536361 ; 2022 (2022)
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Probing Multilingual Cognate Prediction Models
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In: Findings of the Association for Computational Linguistics: ACL 2022 ; https://hal.inria.fr/hal-03614691 ; Findings of the Association for Computational Linguistics: ACL 2022, May 2022, Dublin, Ireland (2022)
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Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?
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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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Towards a Cleaner Document-Oriented Multilingual Crawled Corpus ...
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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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Ungoliant: An Optimized Pipeline for the Generation of a Very Large-Scale Multilingual Web Corpus
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In: CMLC 2021 - 9th Workshop on Challenges in the Management of Large Corpora ; https://hal.inria.fr/hal-03301590 ; CMLC 2021 - 9th Workshop on Challenges in the Management of Large Corpora, Jul 2021, Limerick / Virtual, Ireland. ⟨10.14618/ids-pub-10468⟩ ; https://www.cl2021.org/ (2021)
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First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT
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In: https://hal.inria.fr/hal-03161685 ; 2021 (2021)
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Can Multilingual Language Models Transfer to an Unseen Dialect? A Case Study on North African Arabizi
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In: https://hal.inria.fr/hal-03161677 ; 2021 (2021)
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First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT
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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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When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models
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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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Abstract:
International audience ; Transfer learning based on pretraining language models on a large amount of raw data has become a new norm to reach state-of-theart performance in NLP. Still, it remains unclear how this approach should be applied for unseen languages that are not covered by any available large-scale multilingual language model and for which only a small amount of raw data is generally available. In this work, by comparing multilingual and monolingual models, we show that such models behave in multiple ways on unseen languages. Some languages greatly benefit from transfer learning and behave similarly to closely related high resource languages whereas others apparently do not. Focusing on the latter, we show that this failure to transfer is largely related to the impact of the script used to write such languages. We show that transliterating those languages significantly improves the potential of large-scale multilingual language models on downstream tasks. This result provides a promising direction towards making these massively multilingual models useful for a new set of unseen languages.
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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-03251105/file/NAACL21_Muller_et_al.pdf https://hal.inria.fr/hal-03251105/document https://hal.inria.fr/hal-03251105
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Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
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In: https://hal.inria.fr/hal-03177623 ; 2021 (2021)
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Can Cognate Prediction Be Modelled as a Low-Resource Machine Translation Task?
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In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 ; https://hal.inria.fr/hal-03243380 ; Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Aug 2021, Bangkok, Thailand (2021)
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Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
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In: https://hal.inria.fr/hal-03109187 ; 2021 (2021)
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Variation graphique dans les documents d'Ancien Régime : Nouvelles approches scriptométriques
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In: Journée d’étude : « Pour une histoire de la langue ‘par en bas’: textes privés et variation des langues dans le passé » ; https://hal.inria.fr/hal-03357080 ; Journée d’étude : « Pour une histoire de la langue ‘par en bas’: textes privés et variation des langues dans le passé », Sep 2021, Paris, France (2021)
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Expanding the content model of annotationBlock
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In: Next Gen TEI, 2021 - TEI Conference and Members’ Meeting ; https://hal.archives-ouvertes.fr/hal-03380805 ; Next Gen TEI, 2021 - TEI Conference and Members’ Meeting, Oct 2021, Virtual, United States (2021)
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