Abstract
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 rationale-augmented baselines.- Anthology ID:
- 2022.findings-acl.161
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2022
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2034–2046
- Language:
- URL:
- https://aclanthology.org/2022.findings-acl.161
- DOI:
- 10.18653/v1/2022.findings-acl.161
- Cite (ACL):
- Juanyan Wang, Manali Sharma, and Mustafa Bilgic. 2022. Ranking-Constrained Learning with Rationales for Text Classification. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2034–2046, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Ranking-Constrained Learning with Rationales for Text Classification (Wang et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.findings-acl.161.pdf