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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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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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Abstract:
Building natural language processing systems for non standardized and low resource languages is a difficult challenge. The recent success of large-scale multilingual pretrained language models provides new modeling tools to tackle this. In this work, we study the ability of multilingual language models to process an unseen dialect. We take user generated North-African Arabic as our case study, a resource-poor dialectal variety of Arabic with frequent code-mixing with French and written in Arabizi, a non-standardized transliteration of Arabic to Latin script. Focusing on two tasks, part-of-speech tagging and dependency parsing, we show in zero-shot and unsupervised adaptation scenarios that multilingual language models are able to transfer to such an unseen dialect, specifically in two extreme cases: (i) across scripts, using Modern Standard Arabic as a source language, and (ii) from a distantly related language, unseen during pretraining, namely Maltese. Our results constitute the first successful transfer experiments on this dialect, paving thus the way for the development of an NLP ecosystem for resource-scarce, non-standardized and highly variable vernacular languages.
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Keyword:
[INFO.INFO-TT]Computer Science [cs]/Document and Text Processing
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URL: https://hal.inria.fr/hal-03161677
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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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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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| 10 |
Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios? ...
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First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT ...
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| 12 |
Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell
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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
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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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| 14 |
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models
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In: https://hal.inria.fr/hal-03109106 ; 2020 (2020)
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Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering ...
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When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models ...
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CamemBERT: a Tasty French Language Model
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In: https://hal.inria.fr/hal-02445946 ; 2019 (2019)
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Enhancing BERT for Lexical Normalization
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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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