Majumder, Bodhisattwa Prasad;
Camburu, Oana-Maria;
Lukasiewicz, Thomas;
McAuley, Julian;
(2022)
Knowledge-Grounded Self-Rationalization via
Extractive and Natural Language Explanations.
In: Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan, (eds.)
Proceedings of the 39th International Conference on Machine Learning (PMLR).
(pp. pp. 14786-14801).
PMLR: Baltimore, Maryland, USA.
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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-theart (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.
| Type: | Proceedings paper |
|---|---|
| Title: | Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations |
| Event: | The 39th International Conference on Machine Learning (ICML 2022) |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://proceedings.mlr.press/v162/majumder22a.htm... |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10184050 |
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