Knowledgeable Dialogue Reading Comprehension on Key Turns
This work addresses the problem of improving accuracy in dialogue-based machine reading comprehension for AI systems, though it is incremental as it builds on existing methods with specific enhancements.
The paper tackles the challenges of dialogue-based multi-choice reading comprehension by extracting key turns and incorporating external knowledge, achieving significant improvements over baselines on the DREAM dataset.
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.