Saliency-driven interpretation methods reveal that NMT models learn word alignments of better quality than fast-align under force decoding and consistent with automatic tools under free decoding.
Finch, and Eiichiro Sumita
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Saliency-driven Word Alignment Interpretation for Neural Machine Translation
Saliency-driven interpretation methods reveal that NMT models learn word alignments of better quality than fast-align under force decoding and consistent with automatic tools under free decoding.