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.
Original language | English |
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Title of host publication | Findings of the Association for Computational Linguistics, ACL 2023 |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 11816-11833 |
Number of pages | 18 |
ISBN (Electronic) | 9781959429623 |
DOIs | |
State | Published - 2023 |
Event | Findings of the Association for Computational Linguistics, ACL 2023 - Toronto, Canada Duration: 9 Jul 2023 → 14 Jul 2023 |
Publication series
Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
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ISSN (Print) | 0736-587X |
Conference
Conference | Findings of the Association for Computational Linguistics, ACL 2023 |
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Country/Territory | Canada |
City | Toronto |
Period | 9/07/23 → 14/07/23 |
Bibliographical note
Publisher Copyright:© 2023 Association for Computational Linguistics.
Funding
We are grateful to Diego Frassinelli and the anonymous reviewers for valuable feedback and helpful comments. A. Jacovi and Y. Goldberg received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme, grant agreement No. 802774 (iEXTRACT). N.T. Vu is funded by Carl Zeiss Foundation.
Funders | Funder number |
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Carl-Zeiss-Stiftung | |
Horizon 2020 Framework Programme | 802774 |
European Commission |