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- W4289361879 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." @default.
- W4289361879 created "2022-08-02" @default.
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- W4289361879 date "2018-10-30" @default.
- W4289361879 modified "2023-09-26" @default.
- W4289361879 title "Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model" @default.
- W4289361879 doi "https://doi.org/10.48550/arxiv.1810.12836" @default.
- W4289361879 hasPublicationYear "2018" @default.
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