Saliency-driven Word Alignment Interpretation forNeural Machine Translation
Table 3: Alignment distribution entropy for selected de-en models. att stands for attention in Table 1.
['[EMPTY]', '[BOLD] att', '[ITALIC] σ [BOLD]  = 0', '[ITALIC] σ [BOLD]  = 0.05', '[ITALIC] σ [BOLD]  = 0.15', '[ITALIC] σ [BOLD]  = 0.3']
We run German-English alignments under several different SmoothGrad noise deviation σ and report their dispersion as measured by entropy of the (soft) alignment distribution averaged by number of target words. It should also be noted that the alignment dispersion is consistently lower for free decoding than force decoding. This verifies our conjecture that the force decoding setting might introduce more noise in the model behavior, but judging from this result, that gap seems to be minimal. Comparing different architectures, the dispersion of attention weights does not correlate well with the dispersion of word saliency. We also notice that, while the Transformer attention interpretation consistently results in higher AER, its dispersion is lower than the other architectures, indicating that with attention, a lot of the probability mass might be concentrated in the wrong place more often. This corroborates the finding in Raganato and Tiedemann