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A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages
Open accessAbstract
We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages.We then compare the performance of OSCARbased and Wikipedia-based ELMo embeddings for these languages on the part-ofspeech tagging and parsing tasks.We show that, despite the noise in the Common-Crawlbased OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia.They actually equal or improve the current state of the art in tagging and parsing for all five languages.In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the crosslingual benefit of multilingual embedding architectures.
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“38% consists of text extracted from a pre-processed web crawl, OSCAR (Ortiz Suárez et al. (2020)), filtered with the help of native speakers”
From The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset (2023) · cites this paper as Ortiz Suárez et al. (2020)
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Metadata
- Authors
- Ortiz Suárez, P. J., Romary, L., & Sagot, B.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 1703-1714
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3034617741
- Cited by
- 166
- References
- 0
- Citations by year
- 2026: 6 · 2025: 24 · 2024: 21 · 2023: 26 · 2022: 35 · 2021: 36 · 2020: 18
- FWCI
- 10.98
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Agence Nationale de la Recherche (ANR‐19, ANR-18-CE38-0003, ANR18, ANR-19-P3IA-0001)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- contextual word embeddings, ELMo, Common Crawl, Wikipedia, part-of-speech tagging, syntactic analysis, multilingual BERT, low-resource languages
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