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Multi-style Generative Reading Comprehension
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
This study tackles generative reading comprehension (RC), which consists of answering questions based on textual evidence and natural language generation (NLG). We propose a multi-style abstractive summarization model for question answering, called Masque. The proposed model has two key characteristics. First, unlike most studies on RC that have focused on extracting an answer span from the provided passages, our model instead focuses on generating a summary from the question and multiple
doi:10.18653/v1/p19-1220
dblp:conf/acl/NishidaSNSOAT19
fatcat:qw6qv34umfcwxa5xmn2kh4nmxi