<div class="csl-bib-body">
<div class="csl-entry">Jang, M., Majumder, B. P., McAuley, J., Lukasiewicz, T., & Camburu, O.-M. (2023). KNOW How to Make Up Your Mind! Adversarially Detecting and Remedying Inconsistencies in Natural Language Explanations. In A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), <i>Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</i> (pp. 540–553). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-short.47</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/191167
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dc.description.abstract
While recent works have been considerably improving the quality of the natural language explanations (NLEs) generated by a model to justify its predictions, there is very limited research in detecting and remedying inconsistencies among generated NLEs. In this work, we leverage external knowledge bases to significantly improve on an existing adversarial framework for detecting inconsistent NLEs. We apply our framework to high-performing NLE models and show that models with higher NLE quality do not necessarily generate fewer inconsistencies. Moreover, we propose an off-the-shelf remedy that alleviates NLE inconsistency by injecting external background knowledge into the model. Our remedy decreases the inconsistencies of previous high-performing NLE models as detected by our framework.
en
dc.language.iso
en
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dc.subject
natural language explanation
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dc.subject
Artificial Intelligence
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dc.subject
Deep Neural Networks
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dc.title
KNOW How to Make Up Your Mind! Adversarially Detecting and Remedying Inconsistencies in Natural Language Explanations
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dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
University of Oxford, United Kingdom of Great Britain and Northern Ireland (the)
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dc.contributor.affiliation
University of California, San Diego, United States of America (the)
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dc.contributor.affiliation
University of California, San Diego, United States of America (the)
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dc.contributor.affiliation
University College London, United Kingdom of Great Britain and Northern Ireland (the)
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dc.relation.isbn
978-1-959429-71-5
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dc.relation.doi
10.18653/v1/2023.acl-short.47
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dc.description.startpage
540
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dc.description.endpage
553
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)