  Multi-choice machine reading comprehension (MRC) requires models to choose
the correct answer from candidate options given a passage and a question. Our
research focuses dialogue-based MRC, where the passages are multi-turn
dialogues. It suffers from two challenges, the answer selection decision is
made without support of latently helpful commonsense, and the multi-turn
context may hide considerable irrelevant information. This work thus makes the
first attempt to tackle those two challenges by extracting substantially
important turns and utilizing external knowledge to enhance the representation
of context. In this paper, the relevance of each turn to the question are
calculated to choose key turns. Besides, terms related to the context and the
question in a knowledge graph are extracted as external knowledge. The original
context, question and external knowledge are encoded with the pre-trained
language model, then the language representation and key turns are combined
together with a will-designed mechanism to predict the answer. Experimental
results on a DREAM dataset show that our proposed model achieves great
improvements on baselines.
