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Towards Unsupervised Content Disentanglement in Sentence Representations via Syntactic Roles
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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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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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Abstract:
International audience ; Recent impressive improvements in NLP, largely based on the success of contextual neural language models, have been mostly demonstrated on at most a couple dozen high-resource languages. Building language models and, more generally, NLP systems for non-standardized and low-resource languages remains a challenging task. In this work, we focus on North-African colloquial dialectal Arabic written using an extension of the Latin script, called NArabizi, found mostly on social media and messaging communication. In this low-resource scenario with data displaying a high level of variability, we compare the downstream performance of a character-based language model on part-of-speech tagging and dependency parsing to that of monolingual and multilingual models. We show that a character-based model trained on only 99k sentences of NArabizi and fined-tuned on a small treebank of this language leads to performance close to those obtained with the same architecture pre-trained on large multilingual and monolingual models. Confirming these results a on much larger data set of noisy French user-generated content, we argue that such character-based language models can be an asset for NLP in low-resource and high language variability set-tings.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing
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URL: https://hal.inria.fr/hal-03527328
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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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| 5 |
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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| 7 |
PAGnol: An Extra-Large French Generative Model
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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
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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
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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
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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
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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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| 17 |
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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| 19 |
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering ...
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