How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking
Table 2: Sentiment classification: optimization with DiffMask and REINFORCE (with a moving average baseline for variance reduction) not amortised against erasure exact search. All metrics are computed at token level where optimality is measured at sentence level.
['[BOLD] Metrics', 'REINFORCE+', 'DiffMask']
Erasure search as learning masks Before diving into analysis of the sentiment model, we would like to demonstrate we can learn informative subsets through our differentiable relaxations. In order to do this, we need to have access to the ground-truth. We do not have it for our full approach, but we can obtain it when we do not use amortization (i.e. when all Hard Concrete parameters are learned for a specific example rather than predicted from the BERT states). In that case an optimal solution (or a set of equally good optimal solutions) is provided by erasure. Since erasure requires exact search, and it is unfeasible for long sequences, we evaluate here using only sentences up to 25 words (∼54% of the data). Both achieved a comparable level of sparsity while our method reaches an optimal solution much more often than REINFORCE (45% of the times vs 16%) and is, on average, closer to an optimal solution (87% F1 vs 77% F1).