Multi-style Generative Reading Comprehension

Kyosuke Nishida, Itsumi Saito, Kosuke Nishida, Kazutoshi Shinoda, Atsushi Otsuka, Hisako Asano, Junji Tomita
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
more » ... es. This serves to cover various answer styles required for real-world applications. Second, whereas previous studies built a specific model for each answer style because of the difficulty of acquiring one general model, our approach learns multi-style answers within a model to improve the NLG capability for all styles involved. This also enables our model to give an answer in the target style. Experiments show that our model achieves state-of-the-art performance on the Q&A task and the Q&A + NLG task of MS MARCO 2.1 and the summary task of Nar-rativeQA. We observe that the transfer of the style-independent NLG capability to the target style is the key to its success.
doi:10.18653/v1/p19-1220 dblp:conf/acl/NishidaSNSOAT19 fatcat:qw6qv34umfcwxa5xmn2kh4nmxi