论文标题
U3E：无监督和基于擦除的证据提取机器阅读理解
U3E: Unsupervised and Erasure-based Evidence Extraction for Machine Reading Comprehension
论文作者
论文摘要
机器阅读理解（MRC）的更多任务除了回答预测外，还需要提取支持答案的证据句子。但是，支持证据判决的注释通常是耗时且劳动力密集的。在本文中，为了解决这个问题并考虑到大多数现有的提取方法是半监督的，我们提出了一种无监督的证据提取方法（U3E）。 U3E在文档中删除句子级特征之后的变化是输入，模拟了由于人类记忆力下降而导致的解决问题能力的下降。为了根据完全理解原始文本的语义进行选择，我们还建议度量标准快速为此输入更改选择最佳内存模型。为了将U3E与典型的证据提取方法进行比较并研究其在证据提取方面的有效性，我们在不同数据集上进行实验。实验结果表明，U3E是简单但有效的，不仅更准确地提取证据，而且可以显着改善模型性能。
More tasks in Machine Reading Comprehension(MRC) require, in addition to answer prediction, the extraction of evidence sentences that support the answer. However, the annotation of supporting evidence sentences is usually time-consuming and labor-intensive. In this paper, to address this issue and considering that most of the existing extraction methods are semi-supervised, we propose an unsupervised evidence extraction method (U3E). U3E takes the changes after sentence-level feature erasure in the document as input, simulating the decline in problem-solving ability caused by human memory decline. In order to make selections on the basis of fully understanding the semantics of the original text, we also propose metrics to quickly select the optimal memory model for this input changes. To compare U3E with typical evidence extraction methods and investigate its effectiveness in evidence extraction, we conduct experiments on different datasets. Experimental results show that U3E is simple but effective, not only extracting evidence more accurately, but also significantly improving model performance.
