Abstract
We propose an end-to-end differentiable training paradigm for stable training of a rationalized transformer classifier. Our approach results in a single model that simultaneously classifies a sample and scores input tokens based on their relevance to the classification. To this end, we build on the widely-used three-player-game for training rationalized models, which typically relies on training a rationale selector, a classifier and a complement classifier. We simplify this approach by making a single model fulfill all three roles, leading to a more efficient training paradigm that is not susceptible to the common training instabilities that plague existing approaches. Further, we extend this paradigm to produce class-wise rationales while incorporating recent advances in parameterizing and regularizing the resulting rationales, thus leading to substantially improved and state-of-the-art alignment with human annotations without any explicit supervision.- Anthology ID:
- 2024.emnlp-main.664
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11894–11907
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-main.664/
- DOI:
- 10.18653/v1/2024.emnlp-main.664
- Cite (ACL):
- Marc Felix Brinner and Sina Zarrieß. 2024. Rationalizing Transformer Predictions via End-To-End Differentiable Self-Training. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 11894–11907, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Rationalizing Transformer Predictions via End-To-End Differentiable Self-Training (Brinner & Zarrieß, EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-main.664.pdf