While neural networks have been successfully applied to many natural language processing tasks, they come at the cost of interpretability. In this paper, we propose a general methodology to analyze and interpret decisions from a neural model by observing the effects on the model of erasing various parts of the representation, such as input word-vector dimensions, intermediate hidden units, or input words. We present several approaches to analyzing the effects of such erasure, from computing its impact on evaluation metrics, to using reinforcement learning to erase the minimum set of input words in order to flip a neural model's decision. In a comprehensive analysis of multiple NLP tasks from lexical (word shape, morphology) to sentence-level (sentiment) to document level (sentiment aspect), we show that the proposed methodology not only offers clear explanations about neural model decisions, but also provides a way to conduct error analysis on neural models.
REF employ reinforcement learning to find the optimal words to delete in order to fool the classifier.
Understanding Neural Networks through Representation Erasure
ArXiv
ArXiv
Computer Science