Skip to Main content Skip to Navigation
Conference papers

A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages

Abstract : 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 OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based 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 cross-lingual benefit of multilingual embedding architectures.
Document type :
Conference papers
Complete list of metadatas

Cited literature [40 references]  Display  Hide  Download

https://hal.inria.fr/hal-02863875
Contributor : Pedro Ortiz Suárez <>
Submitted on : Friday, June 12, 2020 - 7:53:42 PM
Last modification on : Friday, June 19, 2020 - 3:27:41 AM

Files

ELMos.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02863875, version 2

Collections

Citation

Pedro Javier Ortiz Suárez, Laurent Romary, Benoît Sagot. A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages. ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle, United States. ⟨hal-02863875v2⟩

Share

Metrics

Record views

34

Files downloads

101