@inproceedings{chen-etal-2026-adversarial,
title = "Adversarial Metric Learning for Fine-Grained Emotion Classification",
author = "Chen, Junfan and
Wu, Sizhe and
Zhang, Richong and
Hu, Chunming",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2089/",
pages = "45087--45099",
ISBN = "979-8-89176-390-6",
abstract = "Fine-grained emotion classification (FEC) requires distinguishing subtly different emotions, where the dominant errors come from closely confusable categories. Recent progress relies on contrastive learning with hard-pair mining, implicitly assuming that a fixed similarity metric is sufficient to optimize informative pairs. We argue that this assumption is fragile because defining whether two utterances are similar becomes a problem when the label space is crowded, and hard-pair mining under a fixed metric can systematically miss the worst confusions. Thus, we treat the similarity function as a learnable component and design an adversarial metric learning (AML) framework. It follows theoretical interpretations of metric-robust representations that better separate confusable emotions. AML trains a pairwise discriminator to maximally confuse two targeted hard pair types, while training the encoder to remain discriminative under this worst-case learned metric. Our code and data are released on GitHub."
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<abstract>Fine-grained emotion classification (FEC) requires distinguishing subtly different emotions, where the dominant errors come from closely confusable categories. Recent progress relies on contrastive learning with hard-pair mining, implicitly assuming that a fixed similarity metric is sufficient to optimize informative pairs. We argue that this assumption is fragile because defining whether two utterances are similar becomes a problem when the label space is crowded, and hard-pair mining under a fixed metric can systematically miss the worst confusions. Thus, we treat the similarity function as a learnable component and design an adversarial metric learning (AML) framework. It follows theoretical interpretations of metric-robust representations that better separate confusable emotions. AML trains a pairwise discriminator to maximally confuse two targeted hard pair types, while training the encoder to remain discriminative under this worst-case learned metric. Our code and data are released on GitHub.</abstract>
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%0 Conference Proceedings
%T Adversarial Metric Learning for Fine-Grained Emotion Classification
%A Chen, Junfan
%A Wu, Sizhe
%A Zhang, Richong
%A Hu, Chunming
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F chen-etal-2026-adversarial
%X Fine-grained emotion classification (FEC) requires distinguishing subtly different emotions, where the dominant errors come from closely confusable categories. Recent progress relies on contrastive learning with hard-pair mining, implicitly assuming that a fixed similarity metric is sufficient to optimize informative pairs. We argue that this assumption is fragile because defining whether two utterances are similar becomes a problem when the label space is crowded, and hard-pair mining under a fixed metric can systematically miss the worst confusions. Thus, we treat the similarity function as a learnable component and design an adversarial metric learning (AML) framework. It follows theoretical interpretations of metric-robust representations that better separate confusable emotions. AML trains a pairwise discriminator to maximally confuse two targeted hard pair types, while training the encoder to remain discriminative under this worst-case learned metric. Our code and data are released on GitHub.
%U https://aclanthology.org/2026.acl-long.2089/
%P 45087-45099
Markdown (Informal)
[Adversarial Metric Learning for Fine-Grained Emotion Classification](https://aclanthology.org/2026.acl-long.2089/) (Chen et al., ACL 2026)
ACL
- Junfan Chen, Sizhe Wu, Richong Zhang, and Chunming Hu. 2026. Adversarial Metric Learning for Fine-Grained Emotion Classification. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 45087–45099, San Diego, California, United States. Association for Computational Linguistics.