Chinese Machine Reading Comprehension Based on Language Model Containing Knowledge

Machine reading comprehension (MRC) is a task that requires machines to answer relevant questions based on a given context. In recent years, it has attracted extensive attention with the development of deep learning and big data. Considering that human beings will associate some external relevant knowledge when understanding the text, researchers have proposed a method of introducing knowledge outside the given context to assist reading and this method is called Knowledge-Based Machine Reading Comprehension (KBMRC). However, the current research on this method is still scattered, and the retrieval and fusion of relevant knowledge are still two challenges in application, especially in Chinese MRC. The contribution of this paper mainly on the following three points: Firstly, in order to resolve the problem of related knowledge retrieval, we build up a related knowledge set. Secondly, in order to resolve the problem of related knowledge fusion, we propose a negative sample generation strategy and train a language model containing knowledge. Finally, a twin-tower fusion model is constructed based on this model. The experiments on Chinese reading comprehension dataset CMRC2018 show that our method has a certain improvement compared with the baseline method without external knowledge.