Incorporating Priors with Feature Attribution on Text Classification

Frederick Liu, Besim Avci
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
Feature attribution methods, proposed recently, help users interpret the predictions of complex models. Our approach integrates feature attributions into the objective function to allow machine learning practitioners to incorporate priors in model building. To demonstrate the effectiveness our technique, we apply it to two tasks: (1) mitigating unintended bias in text classifiers by neutralizing identity terms; (2) improving classifier performance in a scarce data setting by forcing the model
more » ... focus on toxic terms. Our approach adds an L 2 distance loss between feature attributions and task-specific prior values to the objective. Our experiments show that i) a classifier trained with our technique reduces undesired model biases without a tradeoff on the original task; ii) incorporating priors helps model performance in scarce data settings.
doi:10.18653/v1/p19-1631 dblp:conf/acl/LiuA19 fatcat:xwjz46gsgfh2xftmrhkehyczwe