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arXiv · 1810.12836

Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model

Muthuraman Chidambaram·Yinfei Yang·Daniel Cer·Steve Yuan·Yun-Hsuan Sung·Brian Strope·Ray Kurzweil
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Abstract

A significant roadblock in multilingual neural language modeling is the lack of labeled non-English data. One potential method for overcoming this issue is learning cross-lingual text representations that can be used to transfer the performance from training on English tasks to non-English tasks, despite little to no task-specific non-English data. In this paper, we explore a natural setup for learning cross-lingual sentence representations: the dual-encoder. We provide a comprehensive evaluation of our cross-lingual representations on a number of monolingual, cross-lingual, and zero-shot/few-shot learning tasks, and also give an analysis of different learned cross-lingual embedding spaces.

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Muthuraman Chidambaram, Yinfei Yang, Daniel Cer, Steve Yuan, Yun-Hsuan Sung, Brian Strope, Ray Kurzweil. 2018-10-30. Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model. https://arxiv.org/abs/1810.12836

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