Though the community has made great progress on Machine Reading Comprehension
(MRC) task, most of the previous works are solving English-based MRC problems,
and there are few efforts on other languages mainly due to the lack of
large-scale training data. In this paper, we propose Cross-Lingual Machine
Reading Comprehension (CLMRC) task for the languages other than English.
Firstly, we present several back-translation approaches for CLMRC task, which
is straightforward to adopt. However, to accurately align the answer into
another language is difficult and could introduce additional noise. In this
context, we propose a novel model called Dual BERT, which takes advantage of
the large-scale training data provided by rich-resource language (such as
English) and learn the semantic relations between the passage and question in a
bilingual context, and then utilize the learned knowledge to improve reading
comprehension performance of low-resource language. We conduct experiments on
two Chinese machine reading comprehension datasets CMRC 2018 and DRCD. The
results show consistent and significant improvements over various
state-of-the-art systems by a large margin, which demonstrate the potentials in
CLMRC task. Resources available: this https URL

提出了面向非英语语言的跨语言机器阅读理解（CLMRC）任务，通过双向 Bert 模型和回译方法，利用英语作为基础语言的大规模训练数据来提高低资源语言的阅读理解性能，并在中文机器阅读理解数据集上进行了实验，结果表明该方法能够显著提高机器阅读理解的性能。