핵심 개념
The author proposes the SSR method to enhance rationalization by discovering shortcuts, addressing the limitations of existing methods and improving task results.
초록
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.
Key points include:
- Introduction of SSR method for boosting rationalization with shortcuts discovery.
- Strategies to mitigate shortcut issues in prediction tasks.
- Data augmentation techniques for closing the gap between annotated rationales and shortcuts.
- Experimental validation showing SSR outperforming competitive baselines.
통계
Extensive experimental results on real-world datasets validate the effectiveness of our proposed method.
Code is released at https://github.com/yuelinan/codes-of-SSR.
Task F1 and Token F1 scores are provided for different datasets comparing various methods.
인용구
"Since existing methods still suffer from adopting the shortcuts in data to compose rationales..." - Linan Yue et al.
"A well-trained unsupervised rationalization model inevitably composes rationales with both the gold rationale and shortcuts tokens." - Linan Yue et al.