Using Interpretation Methods for Model Enhancement
Zhuo Chen Chengyue Jiang Kewei Tu
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 https://github.com/Chord-Chen-30/UIMER.