@inproceedings{brinner-zarriess-2024-rationalizing,
title = "Rationalizing Transformer Predictions via End-To-End Differentiable Self-Training",
author = "Brinner, Marc Felix and
Zarrie{\ss}, Sina",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-main.664/",
doi = "10.18653/v1/2024.emnlp-main.664",
pages = "11894--11907",
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."
}
Markdown (Informal)
[Rationalizing Transformer Predictions via End-To-End Differentiable Self-Training](https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-main.664/) (Brinner & Zarrieß, EMNLP 2024)
ACL