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Attention models have become a crucial component in neural machinetranslation (NMT). They are often implicitly or explicitly used to justify themodel's decision in generating a specific token but it has not yet beenrigorously established to what extent attention is a reliable source ofinformation in NMT. To evaluate the explanatory power of attention for NMT, weexamine the possibility of yielding the same prediction but with counterfactualattention models that modify crucial aspects of the trained attention model.Using these counterfactual attention mechanisms we assess the extent to whichthey still preserve the generation of function and content words in thetranslation process. Compared to a state of the art attention model, ourcounterfactual attention models produce 68% of function words and 21% ofcontent words in our German-English dataset. Our experiments demonstrate thatattention models by themselves cannot reliably explain the decisions made by aNMT model.

Interrogating the Explanatory Power of Attention in Neural Machine Translation