title: Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model
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 crosslingual sentence representations: the dual-encoder. We provide a comprehensive evaluation of our cross-lingual representations on a number of monolingual, crosslingual, and zero-shot/few-shot learning tasks, and also give an analysis of different learned cross-lingual embedding spaces.
authors: Muthuraman Chidambaram Yinfei Yang Daniel Cer Steve Yuan Yun-Hsuan Sung Brian Strope Ray Kurzweil
hIndex: Daniel Cer 29