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        <title>Improving Multilingual Models with Language-Clustered Vocabularies</title>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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    <abstract>State-of-the-art multilingual models depend on vocabularies that cover all of the languages the model will expect to see at inference time, but the standard methods for generating those vocabularies are not ideal for massively multilingual applications. In this work, we introduce a novel procedure for multilingual vocabulary generation that combines the separately trained vocabularies of several automatically derived language clusters, thus balancing the trade-off between cross-lingual subword sharing and language-specific vocabularies. Our experiments show improvements across languages on key multilingual benchmark tasks TyDi QA (+2.9 F1), XNLI (+2.1%), and WikiAnn NER (+2.8 F1) and factor of 8 reduction in out-of-vocabulary rate, all without increasing the size of the model or data.</abstract>
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%0 Conference Proceedings
%T Improving Multilingual Models with Language-Clustered Vocabularies
%A Chung, Hyung Won
%A Garrette, Dan
%A Tan, Kiat Chuan
%A Riesa, Jason
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F chung-etal-2020-improving
%X State-of-the-art multilingual models depend on vocabularies that cover all of the languages the model will expect to see at inference time, but the standard methods for generating those vocabularies are not ideal for massively multilingual applications. In this work, we introduce a novel procedure for multilingual vocabulary generation that combines the separately trained vocabularies of several automatically derived language clusters, thus balancing the trade-off between cross-lingual subword sharing and language-specific vocabularies. Our experiments show improvements across languages on key multilingual benchmark tasks TyDi QA (+2.9 F1), XNLI (+2.1%), and WikiAnn NER (+2.8 F1) and factor of 8 reduction in out-of-vocabulary rate, all without increasing the size of the model or data.
%R 10.18653/v1/2020.emnlp-main.367
%U https://aclanthology.org/2020.emnlp-main.367
%U https://doi.org/10.18653/v1/2020.emnlp-main.367
%P 4536-4546

Markdown (Informal)

[Improving Multilingual Models with Language-Clustered Vocabularies](https://aclanthology.org/2020.emnlp-main.367) (Chung et al., EMNLP 2020)

• Improving Multilingual Models with Language-Clustered Vocabularies (Chung et al., EMNLP 2020)

ACL

• Hyung Won Chung, Dan Garrette, Kiat Chuan Tan, and Jason Riesa. 2020. Improving Multilingual Models with Language-Clustered Vocabularies. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4536–4546, Online. Association for Computational Linguistics.