{"id":886794,"date":"2022-10-14T08:25:39","date_gmt":"2022-10-14T15:25:39","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2022-10-14T08:25:39","modified_gmt":"2022-10-14T15:25:39","slug":"cross-lingual-machine-reading-comprehension-with-language-branch-knowledge-distillation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/cross-lingual-machine-reading-comprehension-with-language-branch-knowledge-distillation\/","title":{"rendered":"Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation"},"content":{"rendered":"
Cross-lingual Machine Reading Comprehension (CLMRC) remains a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi, and Vietnamese. Many previous approaches use translation data by translating from a rich-source language, such as English, to low-source languages as auxiliary supervision. However, how to effectively leverage translation data and 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