<div class="csl-bib-body">
<div class="csl-entry">Majumder, B. P., Camburu, O.-M., Lukasiewicz, T., & McAuley, J. (2022). Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations. In K. Chaudhuri, S. Jegelka, & L. Song (Eds.), <i>Proceedings of the 39th International Conference on Machine Learning</i> (pp. 14786–14801). MLResearch Press. http://hdl.handle.net/20.500.12708/192473</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/192473
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dc.description.abstract
Models that generate extractive rationales (i.e., subsets of features) or natural language explanations (NLEs) for their predictions are important for explainable AI. While an extractive rationale provides a quick view of the features most responsible for a prediction, an NLE allows for a comprehensive description of the decision-making process behind a prediction. However, current models that generate the best extractive rationales or NLEs often fall behind the state-of-the-art (SOTA) in terms of task performance. In this work, we bridge this gap by introducing RExC, a self-rationalizing framework that grounds its predictions and two complementary types of explanations (NLEs and extractive rationales) in background knowledge. Our framework improves over previous methods by: (i) reaching SOTA task performance while also providing explanations, (ii) providing two types of explanations, while existing models usually provide only one type, and (iii) beating by a large margin the previous SOTA in terms of quality of both types of explanations. Furthermore, a perturbation analysis in RExC shows a high degree of association between explanations and predictions, a necessary property of faithful explanations.
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dc.language.iso
en
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dc.relation.ispartofseries
Proceedings of Machine Learning Research
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dc.subject
natural language explanations
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dc.subject
extractive rationales
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dc.subject
knowledge-grounded self-rationalization
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dc.title
Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations
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dc.type
Inproceedings
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dc.type
Konferenzbeitrag
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dc.contributor.affiliation
University of California San Diego, USA
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dc.contributor.affiliation
University of Oxford, UK
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dc.contributor.affiliation
University of California San Diego, USA
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dc.description.startpage
14786
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dc.description.endpage
14801
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
Proceedings of the 39th International Conference on Machine Learning