论文标题
知识渊博的对话阅读理解
Knowledgeable Dialogue Reading Comprehension on Key Turns
论文作者
论文摘要
多选择机器阅读理解理解（MRC）要求模型从给定段落和问题中从候选选项中选择正确的答案。我们的研究集中于基于对话的MRC，其中段落是多转向对话。它遇到了两个挑战，答案选择决策是在没有潜在有用的常识支持的情况下做出的，多转弯环境可能会隐藏相当大的无关信息。因此，这项工作首次尝试通过提取基本重要的转弯并利用外部知识来增强上下文的表示来解决这两个挑战。在本文中，计算每个回合与问题的相关性以选择关键转弯。此外，将与上下文相关的术语和知识图中的问题作为外部知识提取。原始上下文，问题和外部知识是通过预先训练的语言模型编码的，然后将语言表示和钥匙转弯与意志设计的机制结合在一起，以预测答案。梦境数据集中的实验结果表明，我们提出的模型在基线上取得了重大改进。
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.
