论文标题
杜马：换词思维的阅读理解
DUMA: Reading Comprehension with Transposition Thinking
论文作者
论文摘要
多选择机器阅读理解（MRC）要求模型在给出段落和问题时从一组答案选项中决定正确的答案。因此，除了用作编码器的强大的预训练的语言模型（PRLM）外，多选择的MRC尤其依赖于匹配的网络设计，该设计应有效地捕获通道，问题和答案的三胞胎之间的关系。虽然即使没有匹配网络的支持，但较新，更强大的PRLM即使在没有匹配网络的支持的情况下也表现出了强大，但我们提出了一个新的双重头脑共同注意（DUMA）模型，该模型的灵感来自于人类的转介思维过程求解多选择MRC问题：分别从通过和问题的角度来考虑彼此的焦点。拟议的DUMA已显示出有效，并且能够普遍促进PRLM。我们提出的方法对两个基准的多项选择MRC任务进行了评估，即梦和种族，表明在强大的PRLMS方面，DUMA仍然可以提高模型以达到新的最先进的性能。
Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question. Thus in addition to a powerful Pre-trained Language Model (PrLM) as encoder, multi-choice MRC especially relies on a matching network design which is supposed to effectively capture the relationships among the triplet of passage, question and answers. While the newer and more powerful PrLMs have shown their mightiness even without the support from a matching network, we propose a new DUal Multi-head Co-Attention (DUMA) model, which is inspired by human's transposition thinking process solving the multi-choice MRC problem: respectively considering each other's focus from the standpoint of passage and question. The proposed DUMA has been shown effective and is capable of generally promoting PrLMs. Our proposed method is evaluated on two benchmark multi-choice MRC tasks, DREAM and RACE, showing that in terms of powerful PrLMs, DUMA can still boost the model to reach new state-of-the-art performance.
