Concept-based Abstraction-Aggregation Network forInterpretable Document Classification
Table 1: Averaged accuracy of different models on Newsroom, IMDB, and Beauty testing sets.
['[BOLD] Model', '[BOLD] Newsroom', '[BOLD] IMDB', '[BOLD] Beauty']
We use accuracy as evaluation metric to measure the performance of different models. Comparing BERT-SAN with LSTM-SAN, we find that BERT encoder is better than conventional LSTM encoder with pre-trained word embeddings. We also observe that by replacing SAN with AAN, our model does not significantly lower the classification accuracy, which indicates we can use AAN for the concept-based explanation task without losing the overall performance. BERT-AAN models without dropping aggregation-attention weights still achieve competitive performance compared with other models, however, we find that they attain poor interpretability in concept-based explanation.