Neighboring Words Affect Human Interpretation of Saliency Explanations
Jacovi, Alon, Schuff, Hendrik, Adel, Heike, Vu, Ngoc Thang, Goldberg, Yoav
–arXiv.org Artificial Intelligence
Word-level saliency explanations ("heat maps over words") are often used to communicate feature-attribution in text-based models. Recent studies found that superficial factors such as word length can distort human interpretation of the communicated saliency scores. We conduct a user study to investigate how the marking of a word's neighboring words affect the explainee's perception of the word's importance in the context of a saliency explanation. We find that neighboring words have significant effects on the word's importance rating. Concretely, we identify that the influence changes based on neighboring direction (left vs. right) and a-priori linguistic and computational measures of phrases and collocations (vs. unrelated neighboring words). Our results question whether text-based saliency explanations should be continued to be communicated at word level, and inform future research on alternative saliency explanation methods.
arXiv.org Artificial Intelligence
May-6-2023
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