Improving Multilingual Models With Language-clustered Vocabularies
Hyung Won Chung, Dan Garrette, Kiat Chuan Tan, Jason Riesa . Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) 2020 – 46 citations
[Paper]
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.