Humans read texts at a varying pace, while machine learning models treat each
token in the same way in terms of a computational process. Therefore, we ask,
does it help to make models act more like humans? In this paper, we convert
this intuition into a set of novel models with fixation-guided parallel RNNs or
layers and conduct various experiments on language modeling and sentiment
analysis tasks to test their effectiveness, thus providing empirical validation
for this intuition. Our proposed models achieve good performance on the
language modeling task, considerably surpassing the baseline model. In
addition, we find that, interestingly, the fixation duration predicted by
neural networks bears some resemblance to humans' fixation. Without any
explicit guidance, the model makes similar choices to humans. We also
investigate the reasons for the differences between them, which explain why
"model fixations" are often more suitable than human fixations, when used to
guide language models.

通过固定点引导的并行 RNN 或层，将机器学习模型使其更像人类阅读，并通过语言建模和情感分析任务的实验来测试其有效性，从而提供了经验验证此直觉的证据。我们的模型在语言建模任务上表现出良好的性能，明显优于基准模型，并发现神经网络预测的固定点持续时间与人类的固定点有相似之处。模型在没有明确引导的情况下做出与人类相似的选择，并调查了它们之间差异的原因，解释了为什么 “模型固定点” 在指导语言模型时往往比人类的固定点更合适。