ACL 2023findings4 citations

Neighboring Words Affect Human Interpretation of Saliency Explanations

Alon Jacovi, Hendrik Schuff, Heike Adel, Ngoc Thang Vu, Yoav Goldberg

Abstract

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.

BibTeX
@inproceedings{jacovi-etal-2023-neighboring,
    title = "Neighboring Words Affect Human Interpretation of Saliency Explanations",
    author = "Jacovi, Alon  and
      Schuff, Hendrik  and
      Adel, Heike  and
      Vu, Ngoc Thang  and
      Goldberg, Yoav",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.750/",
    doi = "10.18653/v1/2023.findings-acl.750",
    pages = "11816--11833"
}