We explore whether it is possible to leverage eye-tracking data in an RNN dependency parser (for English) when such information is only available during training, i.e., no aggregated or token-level gaze features are used at inference time. To do so, we train a multitask learning model that parses sentences as sequence labeling and leverages gaze features as auxiliary tasks. Our method also learns to train from disjoint datasets, i.e. it can be used to test whether already collected gaze features are useful to improve the performance on new non-gazed annotated treebanks. Accuracy gains are modest but positive, showing the feasibility of the approach. It can serve as a first step towards architectures that can better leverage eye-tracking data or other complementary information available only for training sentences, possibly leading to improvements in syntactic parsing.

探讨如何在RNN依赖解析器（用于英语）中利用仅在训练期间可用的注视跟踪数据，即推理时间不使用聚合或令牌级注视特征。为此，我们训练了一个多任务学习模型，它将句子解析为序列标签，并利用注视特征作为辅助任务。我们的方法还学习如何从不相交的数据集中训练，即它可以用于测试已经收集的注视特征是否有助于提高在新的非注视注释树库上的性能。精度增益虽然不大但仍是积极的，表明这种方法是可行的。它可以作为一个第一步，朝着能够更好地利用注视跟踪数据或其他仅在训练句子时可用的补充信息的体系结构，可能导致在句法解析中的性能改进。

使依存句法分析器具备视觉能力