Zero-Shot Cross-Lingual Transfer with Meta Learning

Farhad Nooralahzadeh, Giannis Bekoulis, Johannes Bjerva, Isabelle Augenstein

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

Learning what to share between tasks has become a topic of great importance, as strategic sharing of knowledge has been shown to improve downstream task performance. This is particularly important for multilingual applications, as most languages in the world are under-resourced. Here, we consider the setting of training models on multiple different languages at the same time, when little or no data is available for languages other than English. We show that this challenging setup can be approached using meta-learning: in addition to training a source language model, another model learns to select which training instances are the most beneficial to the first. We experiment using standard supervised, zero-shot cross-lingual, as well as few-shot cross-lingual settings for different natural language understanding tasks (natural language inference, question answering). Our extensive experimental setup demonstrates the consistent effectiveness of meta-learning for a total of 15 languages. We improve upon the state-of-the-art for zero-shot and few-shot NLI (on MultiNLI and XNLI) and QA (on the MLQA dataset). A comprehensive error analysis indicates that the correlation of typological features between languages can partly explain when parameter sharing learned via meta-learning is beneficial.
Original languageEnglish
Title of host publicationProceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Number of pages16
Place of PublicationOnline
PublisherAssociation for Computational Linguistics
Publication date1 Nov 2020
Pages4547-4562
DOIs
Publication statusPublished - 1 Nov 2020
EventThe 2020 Conference on Empirical Methods in Natural Language Processing -
Duration: 16 Nov 202020 Nov 2020
https://2020.emnlp.org/

Conference

ConferenceThe 2020 Conference on Empirical Methods in Natural Language Processing
Period16/11/202020/11/2020
Internet address

Keywords

  • Natural Language Processing
  • Machine Learning

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