  RNN models have achieved the state-of-the-art performance in a wide range of
text mining tasks. However, these models are often regarded as black-boxes and
are criticized due to the lack of interpretability. In this paper, we enhance
the interpretability of RNNs by providing interpretable rationales for RNN
predictions. Nevertheless, interpreting RNNs is a challenging problem. Firstly,
unlike existing methods that rely on local approximation, we aim to provide
rationales that are more faithful to the decision making process of RNN models.
Secondly, a flexible interpretation method should be able to assign
contribution scores to text segments of varying lengths, instead of only to
individual words. To tackle these challenges, we propose a novel attribution
method, called REAT, to provide interpretations to RNN predictions. REAT
decomposes the final prediction of a RNN into additive contribution of each
word in the input text. This additive decomposition enables REAT to further
obtain phrase-level attribution scores. In addition, REAT is generally
applicable to various RNN architectures, including GRU, LSTM and their
bidirectional versions. Experimental results demonstrate the faithfulness and
interpretability of the proposed attribution method. Comprehensive analysis
shows that our attribution method could unveil the useful linguistic knowledge
captured by RNNs. Some analysis further demonstrates our method could be
utilized as a debugging tool to examine the vulnerability and failure reasons
of RNNs, which may lead to several promising future directions to promote
generalization ability of RNNs.

Computation and Language;Machine Learning