Natural Response Generation for Chinese Reading Comprehension
Nuo Chen, Hongguang Li, Yinan Bao, Baoyuan Wang, Jia Li · 2023
Machine reading comprehension (MRC) is an important area of conversation agents and draws a lot of attention.However, there is a notable limitation to current MRC benchmarks: The labeled answers are mostly either spans extracted from the target corpus or the choices of the given candidates, ignoring the natural aspect of high-quality responses.As a result, MRC models trained on these datasets can not generate human-like responses in real QA scenarios.To this end, we construct a new dataset called Penguin to promote the research of MRC, providing a training and test bed for natural response generation to real scenarios.Concretely, Penguin consists of 200k training data with high-quality fluent, and well-informed responses.Penguin is the first benchmark towards natural response generation in Chinese MRC on a relatively large scale.To address the challenges in Penguin, we develop two strong baselines: endto-end and two-stage frameworks.Following that, we further design Prompt-BART: finetuning the pre-trained generative language models with a mixture of prefix prompts in Penguin.Extensive experiments validated the effectiveness of this design.Our benchmark and codes are available at https://github. com/nuochenpku/Penguin.