On Attribution of Recurrent Neural Network Predictions via Additive Decomposition
Table 5. Interpretability statistical comparison (in percent) of our method with baseline RNN attribution methods.
['[BOLD] Models', '[BOLD] SST2 GRU', '[BOLD] SST2 LSTM', '[BOLD] SST2 BiGRU', '[BOLD] Yelp GRU', '[BOLD] Yelp LSTM', '[BOLD] Yelp BiGRU']
We compare the interpretability score of the proposed method with baseline methods on three RNN architectures over SST2 and Yelp dataset. The results are presented in Tab. The proposed method ranks highest for five tasks among all six classification tasks. We observe that the interpretability scores for the NaiveREAT method, which is introduced in Sec. Sometimes, NaiveREAT could accurately capture the contribution score for strong sentiment word, such as for BiGRU prediction on SST2. In other cases, it may assign some false positive and false negative attribution values, leading to lower interpretability scores. Another interesting finding is that the interpretability of InteGrad does not outperform VanillaGrad consistently on all cases. Besides, GradInput yields consistently better performance comparing to VanillaGrad.