Multi-style Generative Reading Comprehension

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. Moreover, they cannot utilize and control different styles of answers, such as concise phrases and well-formed sentences, within a model. In this study, we propose a style-controllable Multi-source Abstractive Summarization model for QUEstion answering, called Masque. The model is an end-to-end deep neural network that can generate answers conditioned on a given style. Experiments with MS MARCO 2.1 show that our model achieved state-of-the-art performance on two tasks with different answer styles.