[Purpose] To understand the meaning of a sentence, humans can focus on important words in the sentence, which reflects our eyes staying on each word in different gaze time or times. Thus, some studies utilize eye-tracking values to optimize the attention mechanism in deep learning models. But these studies lack to explain the rationality of this approach. Whether the attention mechanism possesses this feature of human reading needs to be explored. [Design/methodology/approach] We conducted experiments on a sentiment classification task. Firstly, we obtained eye-tracking values from two open-source eye-tracking corpora to describe the feature of human reading. Then, the machine attention values of each sentence were learned from a sentiment classification model. Finally, a comparison was conducted to analyze machine attention values and eye-tracking values. [Findings] Through experiments, we found the attention mechanism can focus on important words, such as adjectives, adverbs, and sentiment words, which are valuable for judging the sentiment of sentences on the sentiment classification task. It possesses the feature of human reading, focusing on important words in sentences when reading. Due to the insufficient learning of the attention mechanism, some words are wrongly focused. The eye-tracking values can help the attention mechanism correct this error and improve the model performance. [Originality/value] Our research not only provides a reasonable explanation for the study of using eye-tracking values to optimize the attention mechanism, but also provides new inspiration for the interpretability of attention mechanism.
翻译:为了理解判决的含义,人类可以侧重于句子中的重要词句,这反映了我们在不同的时间或时间对每个字的注意。因此,一些研究利用跟踪视值优化深层学习模式的注意机制。但是这些研究缺乏解释这一方法的合理性。关注机制是否具有人类阅读的这一特征需要探索。[设计/方法/方 我们对感知分类任务进行了实验。首先,我们从两个开放源码的跟踪视觉的团体中获得了跟踪视觉灵感值,以描述人类阅读的特征。然后,从情绪分类模式中学习了每个句子的机器注意值。最后,进行了比较,以分析机器注意值和跟踪值。通过实验,我们发现关注机制可以侧重于重要的词句,如感知、动词和感知词,这些词对于判断感知分类任务中的判决感知感是有价值的。它拥有人类阅读的特征,在阅读时侧重于重要的词句,而不是在阅读时的注意力分类模型。最后,通过分析分析分析分析机器注意和跟踪值。通过实验,我们的重点机制的注意力机制可以提供正确的注意力机制,只有判断对感知性解释。