Improving Natural Language Processing T asks with Human Gaze-Guided Neural Attention: Supplementary Material

To gain further insight into the comparison between our model and the current state of the art in sentence compression, we show results of our method and ablations in relation to ablations of the method by Zhao et al. (see Table 1). In their work, the authors added a "syntax-based Also shown is the number of model parameters. We show that our model, without additional syntactic information as was used in previous methods, still obtains SOT A performance. Figure 1: Additional paraphrase generation attention maps from our ablation study, for both sub-networks (TSM predictions and upstream task attention) in our joint architecture. TSM fixation predictions (left in blue) over epochs (last epoch is our converged models). However, we assume they do not play a role in performance between these two conditions, as these performance differences are not statistically significant.