Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?
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| Format: | Preprint |
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2021
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| _version_ | 1866915318565175296 |
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| author | Riabi, Arij Sagot, Benoît Seddah, Djamé |
| author_facet | Riabi, Arij Sagot, Benoît Seddah, Djamé |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2110_13658 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios? Riabi, Arij Sagot, Benoît Seddah, Djamé Computation and Language Machine Learning 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. |
| title | Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios? |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2110.13658 |