Despite recent work in Reading Comprehension (RC), progress has been mostly
limited to English due to the lack of large-scale datasets in other languages.
In this work, we introduce the first RC system for languages without RC
training data. Given a target language without RC training data and a pivot
language with RC training data (e.g. English), our method leverages existing RC
resources in the pivot language by combining a competitive RC model in the
pivot language with an attentive Neural Machine Translation (NMT) model. We
first translate the data from the target to the pivot language, and then obtain
an answer using the RC model in the pivot language. Finally, we recover the
corresponding answer in the original language using soft-alignment attention
scores from the NMT model. We create evaluation sets of RC data in two
non-English languages, namely Japanese and French, to evaluate our method.
Experimental results on these datasets show that our method significantly
outperforms a back-translation baseline of a state-of-the-art product-level
machine translation system.

本研究提出一种基于注意力机制的神经机器翻译模型，将源语言（非英语）数据经过翻译转化为目标语言（英语），在英语翻译数据上训练阅读理解模型，在目标语言中生成答案并通过软对齐注意力机制将目标语言中的答案还原回源语言，并且实验表明，我们的方法明显优于最先进的机器翻译系统的回译基线方法。