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With a large number of datasets being released and new techniques being proposed, Question answering (QA) systems have witnessed great breakthroughs in reading comprehension (RC) tasks. However, most existing methods focus on improving in-domain performance, leaving open the research question of how these models and techniques can generalize to out-ofdomain and unseen RC tasks. To enhance the generalization ability, we propose a multi-task learning framework that learns the shareddoi:10.18653/v1/d19-5827 dblp:conf/acl-mrqa/SuXWXKLF19 fatcat:wtcgfgoua5hejot4jmeefucag4