  Attention mechanisms have recently demonstrated impressive performance on a
range of NLP tasks, and attention scores are often used as a proxy for model
explainability. However, there is a debate on whether attention weights can, in
fact, be used to identify the most important inputs to a model. We approach
this question from an information theoretic perspective by measuring the mutual
information between the model output and the hidden states. From extensive
experiments, we draw the following conclusions: (i) Additive and Deep attention
mechanisms are likely to be better at preserving the information between the
hidden states and the model output (compared to Scaled Dot-product); (ii)
ablation studies indicate that Additive attention can actively learn to explain
the importance of its input hidden representations; (iii) when attention values
are nearly the same, the rank order of attention values is not consistent with
the rank order of the mutual information(iv) Using Gumbel-Softmax with a
temperature lower than one, tends to produce a more skewed attention score
distribution compared to softmax and hence is a better choice for explainable
design; (v) some building blocks are better at preserving the correlation
between the ordered list of mutual information and attention weights order (for
e.g., the combination of BiLSTM encoder and Additive attention). Our findings
indicate that attention mechanisms do have the potential to function as a
shortcut to model explanations when they are carefully combined with other
model elements.
