The remarkable success in neural networks provokes the selective rationalization. It explains the prediction results by identifying a small subset of the inputs sufficient to support them. Since existing methods still suffer from adopting the shortcuts in data to compose rationales and limited large-scale annotated rationales by human, in this paper, we propose a Shortcuts-fused Selective Rationalization (SSR) method, which boosts the rationalization by discovering and exploiting potential shortcuts. Specifically, SSR first designs a shortcuts discovery approach to detect several potential shortcuts. Then, by introducing the identified shortcuts, we propose two strategies to mitigate the problem of utilizing shortcuts to compose rationales. Finally, we develop two data augmentations methods to close the gap in the number of annotated rationales. Extensive experimental results on real-world datasets clearly validate the effectiveness of our proposed method.

翻译：神经网络取得的显著成功催生了选择性合理化方法，该方法通过识别足以支撑预测结果的输入子集来解释预测结果。由于现有方法仍存在利用数据中的捷径构建合理化依据以及人工标注的大规模合理化依据有限的问题，本文提出了一种融合捷径的选择性合理化方法，该方法通过发现并利用潜在捷径来增强合理化效果。具体而言，SSR首先设计了一种捷径发现方法来检测若干潜在捷径；随后通过引入已识别的捷径，提出两种策略来缓解利用捷径构建合理化依据的问题；最后开发了两种数据增强方法以弥补标注合理化依据的数量缺口。在真实数据集上的大量实验结果清晰验证了所提方法的有效性。