Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?

• URL: http://arxiv.org/abs/2110.13658v1
• Date: Tue, 26 Oct 2021 14:59:16 GMT
• Title: Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?
• Authors: Arij Riabi, Beno\^it Sagot, Djam\'e Seddah
• Abstract summary: We focus on North-African colloquial dialectal Arabic written using an extension of the Latin script, called NArabizi. We show that a character-based model trained on only 99k sentences of NArabizi and fined-tuned on a small treebank leads to performance close to those obtained with the same architecture pre-trained on large multilingual and monolingual models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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