Multi-style Generative Reading Comprehension

8 Jan 2019Kyosuke Nishida • Itsumi Saito • Kosuke Nishida • Kazutoshi Shinoda • Atsushi Otsuka • Hisako Asano • Junji Tomita

This study focuses on the task of multi-passage reading comprehension (RC) where an answer is provided in natural language. Current mainstream approaches treat RC by extracting the answer span from the provided passages and cannot generate an abstractive summary from the given question and passages. In this study, we propose a style-controllable Multi-source Abstractive Summarization model for QUEstion answering, called Masque.

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Task Dataset Model Metric name Metric value Global rank Compare
Question Answering MS MARCO Masque Q&A Style Rouge-L 52.20 # 1
Question Answering MS MARCO Masque Q&A Style BLEU-1 43.77 # 3