Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing 2023
DOI: 10.18653/v1/2023.emnlp-main.700
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Interventional Rationalization

Linan Yue,
Qi Liu,
Li Wang
et al.

Abstract: Selective rationalizations improve the explainability of neural networks by selecting a subsequence of the input (i.e., rationales) to explain the prediction results. Although existing methods have achieved promising results, they still suffer from adopting the spurious correlations in data (aka., shortcuts) to compose rationales and make predictions. Inspired by the causal theory, in this paper, we develop an interventional rationalization (Inter-RAT) to discover the causal rationales. Specifically, we first … Show more

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