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: end-to-end
and two-stage frameworks. Following that, we further design Prompt-BART:
fine-tuning the pre-trained generative language models with a mixture of prefix
prompts in Penguin. Extensive experiments validated the effectiveness of this
design.

该研究构建了一个名为 Penguin 的新数据集，用于促进自然反应生成的 MRC 研究，并提供训练和测试场地。研究人员通过对机器阅读理解模型的测试，采用基于前缀提示的模型，证明了该模型的有效性。