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Register a new userModeling Task Effects in Human Reading with Neural Attention
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Added by
Michael Hahn
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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Humans read by making a sequence of fixations and saccades. They often skip words, without apparent detriment to understanding. We offer a novel explanation for skipping: readers optimize a tradeoff between performing a language-related task and fixating as few words as possible. We propose a neural architecture that combines an attention module (deciding whether to skip words) and a task module (memorizing the input). We show that our model predicts human skipping behavior, while also modeling reading times well, even though it skips 40% of the input. A key prediction of our model is that different reading tasks should result in different skipping behaviors. We confirm this prediction in an eye-tracking experiment in which participants answers questions about a text. We are able to capture these experimental results using the our model, replacing the memorization module with a task module that performs neural question answering.
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Attention is a key mechanism for information selection in both biological brains and many state-of-the-art deep neural networks (DNNs). Here, we investigate whether humans and DNNs allocate attention in comparable ways when reading a text passage to subsequently answer a specific question. We analyze 3 transformer-based DNNs that reach human-level performance when trained to perform the reading comprehension task. We find that the DNN attention distribution quantitatively resembles human attention distribution measured by fixation times. Human readers fixate longer on words that are more relevant to the question-answering task, demonstrating that attention is modulated by top-down reading goals, on top of lower-level visual and text features of the stimulus. Further analyses reveal that the attention weights in DNNs are also influenced by both top-down reading goals and lower-level stimulus features, with the shallow layers more strongly influenced by lower-level text features and the deep layers attending more to task-relevant words. Additionally, deep layers attention to task-relevant words gradually emerges when pre-trained DNN models are fine-tuned to perform the reading comprehension task, which coincides with the improvement in task performance. These results demonstrate that DNNs can evolve human-like attention distribution through task optimization, which suggests that human attention during goal-directed reading comprehension is a consequence of task optimization.
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