论文标题
无监督的解释生成机器阅读理解
Unsupervised Explanation Generation for Machine Reading Comprehension
论文作者
论文摘要
随着各种预训练的语言模型（PLM）的盛开，机器阅读理解（MRC）可以对各种基准，甚至超过人类的表现进行了重大改进。但是，现有作品仅针对最终预测的准确性，而忽略了解释对预测的重要性，这是在现实生活中利用这些模型来说服人类时的大障碍。在本文中，我们为机器阅读理解任务提出了一个可自我解释的框架。主要思想是，与使用整个段落的系统相比，所提出的系统试图使用较少的段落信息并获得相似的结果，而过滤后的段落将用作解释。我们对三个多项选择的MRC数据集进行了实验，发现所提出的系统可以对基线系统实现一致的改进。为了评估解释性，我们将我们的方法与人类评估中的传统注意力机制进行了比较，发现所提出的系统比后者具有显着优势。
With the blooming of various Pre-trained Language Models (PLMs), Machine Reading Comprehension (MRC) has embraced significant improvements on various benchmarks and even surpass human performances. However, the existing works only target on the accuracy of the final predictions and neglect the importance of the explanations for the prediction, which is a big obstacle when utilizing these models in real-life applications to convince humans. In this paper, we propose a self-explainable framework for the machine reading comprehension task. The main idea is that the proposed system tries to use less passage information and achieve similar results compared to the system that uses the whole passage, while the filtered passage will be used as explanations. We carried out experiments on three multiple-choice MRC datasets, and found that the proposed system could achieve consistent improvements over baseline systems. To evaluate the explainability, we compared our approach with the traditional attention mechanism in human evaluations and found that the proposed system has a notable advantage over the latter one.
