论文标题
桥接信息寻求人类的目光和机器阅读理解
Bridging Information-Seeking Human Gaze and Machine Reading Comprehension
论文作者
论文摘要
在这项工作中，我们分析了人类在阅读理解过程中的凝视如何根据给定的阅读理解问题进行条件，以及该信号是否对机器阅读理解有益。为此，我们收集了一个新的引人注目的数据集，其中大量参与者从事多项选择阅读理解任务。我们对这些数据的分析表明，在文本的部分内容上，固定时间增加了，这与回答问题最相关。在这一发现的激励下，我们提出，通过模仿阅读理解过程中的人类信息寻求阅读行为，使自动阅读理解更像人性化。我们证明，这种方法会导致在英语中以最新的阅读理解模型回答多项选择问题的性能。
In this work, we analyze how human gaze during reading comprehension is conditioned on the given reading comprehension question, and whether this signal can be beneficial for machine reading comprehension. To this end, we collect a new eye-tracking dataset with a large number of participants engaging in a multiple choice reading comprehension task. Our analysis of this data reveals increased fixation times over parts of the text that are most relevant for answering the question. Motivated by this finding, we propose making automated reading comprehension more human-like by mimicking human information-seeking reading behavior during reading comprehension. We demonstrate that this approach leads to performance gains on multiple choice question answering in English for a state-of-the-art reading comprehension model.
