Deep learning has emerged as a compelling solution to many NLP tasks with
remarkable performances. However, due to their opacity, such models are hard to
interpret and trust. Recent work on explaining deep models has introduced
approaches to provide insights toward the model's behaviour and predictions,
which are helpful for assessing the reliability of the model's predictions.
However, such methods do not improve the model's reliability. In this paper, we
aim to teach the model to make the right prediction for the right reason by
providing explanation training and ensuring the alignment of the model's
explanation with the ground truth explanation. Our experimental results on
multiple tasks and datasets demonstrate the effectiveness of the proposed
method, which produces more reliable predictions while delivering better
results compared to traditionally trained models.

本论文旨在通过提供解释训练并确保模型的解释与真实解释的一致性，教会深度学习模型以恰当的原因做出正确的预测。实验结果表明，所提出的方法比传统训练模型更加可靠和有效。

显著性学习：教导模型关注重点

Saliency Learning: Teaching the Model Where to Pay Attention

Deep models are state-of-the-art for many vision tasks including video action
recognition and video captioning. Models are trained to caption or classify
activity in videos, but little is known about the evidence used to make such
decisions. Grounding decisions made by deep networks has been studied in
spatial visual content, giving more insight into model predictions for images.
However, such studies are relatively lacking for models of spatiotemporal
visual content - videos. In this work, we devise a formulation that
simultaneously grounds evidence in space and time, in a single pass, using
top-down saliency. We visualize the spatiotemporal cues that contribute to a
deep model's classification/captioning output using the model's internal
representation. Based on these spatiotemporal cues, we are able to localize
segments within a video that correspond with a specific action, or phrase from
a caption, without explicitly optimizing/training for these tasks.

本研究提出了一种基于深度模型的视频内容分析方法，同时利用自顶向下的显著地图来将证据同时固定在空间和时间上，并通过模型内部表示可视化出影响深度模型输出的时空线索，进而能够在视频段中定位特定动作或字幕短语所对应的特征。