Multilingual Extractive Reading Comprehension by Runtime Machine Translation
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Existing end-to-end neural network models for extractive Reading Comprehension (RC) have enjoyed the benefit of a large amount of hand-annotated training data. However, such a dataset is usually available only in English, which limits one from building an extractive RC model for a language of interest. In this paper, we introduce the first extractive RC systems for non-English languages without using language-specific RC training data, but instead by using an English RC model and an attention-based Neural Machine Translation (NMT) model. To train the NMT model for specific language directions, we take advantage of constantly growing web resources to automatically construct parallel corpora, rather than assuming the availability of high quality parallel corpora of the target domain. Our method first translates a paragraph-question pair into English so that the English extractive RC model can output its answer. The attention mechanism in the NMT model is further used to directly align the answer in the target text of interest. Experimental results in two non-English languages, namely Japanese and French, show that our method significantly outperforms a back-translation baseline of a state-of-the-art product-level machine translation system. Moreover, our ablation studies suggest that adding a small number of manually translated questions, besides an automatically created corpus, could further improve the performance of the extractive RC systems for non-English languages.
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