Explaining a Neural Attention Model for Aspect-Based Sentiment Classification Using Diagnostic Classification
This work addresses the interpretability issue in aspect-based sentiment analysis for researchers and practitioners, but it is incremental as it builds on existing models without introducing a new paradigm.
The paper tackles the problem of explaining black-box neural attention models for Aspect-Based Sentiment Classification by proposing explanation models using Diagnostic Classification to inspect the internal dynamics of the LCR-Rot-hop model, finding that lower layers encode part-of-speech and sentiment values while higher layers represent aspect relations and aspect-related sentiment values.
Many high performance machine learning models for Aspect-Based Sentiment Classification (ABSC) produce black box models, and therefore barely explain how they classify a certain sentiment value towards an aspect. In this paper, we propose explanation models, that inspect the internal dynamics of a state-of-the-art neural attention model, the LCR-Rot-hop, by using a technique called Diagnostic Classification. Our diagnostic classifier is a simple neural network, which evaluates whether the internal layers of the LCR-Rot-hop model encode useful word information for classification, i.e., the part of speech, the sentiment value, the presence of aspect relation, and the aspect-related sentiment value of words. We conclude that the lower layers in the LCR-Rot-hop model encode the part of speech and the sentiment value, whereas the higher layers represent the presence of a relation with the aspect and the aspect-related sentiment value of words.