Multilingual pre-trained models could leverage the training data from a rich
source language (such as English) to improve performance on low resource
languages. However, the transfer quality for multilingual Machine Reading
Comprehension (MRC) is significantly worse than sentence classification tasks
mainly due to the requirement of MRC to detect the word level answer boundary.
In this paper, we propose two auxiliary tasks in the fine-tuning stage to
create additional phrase boundary supervision: (1) A mixed MRC task, which
translates the question or passage to other languages and builds cross-lingual
question-passage pairs; (2) A language-agnostic knowledge masking task by
leveraging knowledge phrases mined from web. Besides, extensive experiments on
two cross-lingual MRC datasets show the effectiveness of our proposed approach.

本文提出在 fine-tuning 阶段加入两个辅助任务，建立跨语言问题 - 段落对，并结合从 web 挖掘的知识短语进行跨语言 MRC，实验证明该方法有效。