Explaining with Contrastive Phrasal Highlighting: A Case Study in
  Assisting Humans to Detect Translation Differences
Eleftheria Briakou Navita Goyal Marine Carpuat
Explainable NLP techniques primarily explain by answering "Which tokens in
the input are responsible for this prediction?''. We argue that for NLP models
that make predictions by comparing two input texts, it is more useful to
explain by answering "What differences between the two inputs explain this
prediction?''. We introduce a technique to generate contrastive highlights that
explain the predictions of a semantic divergence model via
phrase-alignment-guided erasure. We show that the resulting highlights match
human rationales of cross-lingual semantic differences better than popular
post-hoc saliency techniques and that they successfully help people detect
fine-grained meaning differences in human translations and critical machine
translation errors.