In the age of neural natural language processing, there are plenty of works
trying to derive interpretations of neural models. Intuitively, when gold
rationales exist during training, one can additionally train the model to match
its interpretation with the rationales. However, this intuitive idea has not
been fully explored. In this paper, we propose a framework of utilizing
interpretation methods and gold rationales to enhance models. Our framework is
very general in the sense that it can incorporate various interpretation
methods. Previously proposed gradient-based methods can be shown as an instance
of our framework. We also propose two novel instances utilizing two other types
of interpretation methods, erasure/replace-based and extractor-based methods,
for model enhancement. We conduct comprehensive experiments on a variety of
tasks. Experimental results show that our framework is effective especially in
low-resource settings in enhancing models with various interpretation methods,
and our two newly-proposed methods outperform gradient-based methods in most
settings. Code is available at this https URL

在神经自然语言处理时代，有很多作品试图得出神经模型的解释。本文提出了一种利用解释方法和黄金理论来增强模型的框架，该框架通用性强，可以融合多种解释方法，并且在低资源环境中特别有效。