On Attribution of Recurrent Neural Network Predictions via Additive Decomposition
Table 4. Comparison about attribution faithfulness between our method and the baseline methods.
['[BOLD] Models', 'GRU', 'LSTM', 'BiGRU']
We search in SST2 dataset for texts that contain two sentences and use the word “but” as conjunction, and obtain a set with 142 texts. The faithfulness scores for different attribution methods on SST2 dataset are reported in Tab. The proposed method consistently outperforms the baseline methods for all the three architectures. This result demonstrates two advantages of REAT: (1) the generated interpretations are highly faithful to original RNN, (2) the generated phrase-level interpretations are accurate. Since the NaiveREAT method does not consider the forgetting mechanism, it may assign false positive contribution scores for the first sentence in the testing texts. As a result, it is not faithful to the target model and achieves relatively low faithfulness score comparing to REAT. Besides, for the other baseline methods, they can only output word-level attribution scores. Since the word-level scores are not sufficiently faithful to the target model, the sentence-level scores calculated by summing up the word-level scores will further deviate from the prediction of the target RNN model. As a result, these methods have only limited faithfulness performance.