Multilingual Extractive Reading Comprehension by Runtime Machine Translation Akari Asaiy, Akiko Eriguchiy, Kazuma Hashimotoz, and Yoshimasa Tsuruokay yThe University of Tokyo zSalesforce Research
[email protected] zferiguchi,
[email protected] [email protected] Abstract to English. To alleviate the scarcity of training data in non- Despite recent work in Reading Comprehen- English languages, previous work creates a new sion (RC), progress has been mostly limited to English due to the lack of large-scale datasets large-scale dataset for a language of interest (He in other languages. In this work, we introduce et al., 2017) or combines a medium-scale dataset the first RC system for languages without RC in the language with an existing dataset translated training data. Given a target language without from English (Lee et al., 2018). These efforts in RC training data and a pivot language with RC data creation are often costly, and must be repeated training data (e.g. English), our method lever- for each new language of interest. In addition, they ages existing RC resources in the pivot lan- do not leverage existing resources in English RC, guage by combining a competitive RC model such as the wealth of large-scale datasets and state- in the pivot language with an attentive Neural Machine Translation (NMT) model. We first of-the-art models. translate the data from the target to the pivot In this paper, we propose a multilingual extrac- language, and then obtain an answer using the tive RC method by runtime Machine Translation RC model in the pivot language.