@inproceedings{wang-etal-2022-ranking,
title = "Ranking-Constrained Learning with Rationales for Text Classification",
author = "Wang, Juanyan and
Sharma, Manali and
Bilgic, Mustafa",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.161",
doi = "10.18653/v1/2022.findings-acl.161",
pages = "2034--2046",
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.",
}
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%0 Conference Proceedings
%T Ranking-Constrained Learning with Rationales for Text Classification
%A Wang, Juanyan
%A Sharma, Manali
%A Bilgic, Mustafa
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F wang-etal-2022-ranking
%X 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.
%R 10.18653/v1/2022.findings-acl.161
%U https://aclanthology.org/2022.findings-acl.161
%U https://doi.org/10.18653/v1/2022.findings-acl.161
%P 2034-2046
Markdown (Informal)
[Ranking-Constrained Learning with Rationales for Text Classification](https://aclanthology.org/2022.findings-acl.161) (Wang et al., Findings 2022)
ACL