The paper introduces the Shortcuts-fused Selective Rationalization (SSR) method to boost rationalization by identifying and utilizing potential shortcuts. It combines unsupervised and supervised approaches, develops strategies to mitigate shortcut issues, and augments data for improved performance. Experimental results validate SSR's effectiveness in real-world datasets.
The research focuses on enhancing selective rationalization by leveraging shortcuts in data to improve task results and generate more plausible rationales. By combining unsupervised and supervised methods, SSR addresses the challenges of exploiting shortcuts while composing rationales. The proposed strategies aim to close the gap between annotated rationales and shortcuts for more accurate predictions.
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by Linan Yue,Qi... às arxiv.org 03-14-2024
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