Ranking-Constrained Learning with Rationales for Text Classification
Juanyan Wang, Manali Sharma, Mustafa Bilgic · Findings of the Association for Computational Linguistics: ACL 2022 · 2022
We propose a novel approach that jointly utilizes the labels and elicited rationales for text classification to speed up the training of deep learning models with limited training data.We define and optimize a ranking-constrained loss function that combines cross-entropy loss with ranking losses as rationale constraints.We evaluate our proposed rationale-augmented learning approach on three human-annotated datasets, and show that our approach provides significant improvements over classification approaches that do not utilize rationales as well as other state-of-the-art rationaleaugmented baselines.