@inproceedings{lin-etal-2026-learning,
title = "Learning What Is Said, Not How: Stance-Contrastive Learning for Generalizable Hate Speech Detection",
author = "Lin, Yucheng and
Kienast, Dan and
Jiang, Jiaojiao",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.61/",
pages = "1301--1318",
ISBN = "979-8-89176-439-2",
abstract = "Hate speech classifiers often misclassify counter speech, including condemnation and quotation of hateful content, as hateful because they fail to distinguish propositional content from speaker stance. We identify this issue as content stance conflation and address it as a representation learning problem. We learn stance invariant representations of the same proposition by constructing contrastive pairs that share content but differ in stance. We implement this idea through factorized annotation, stance controlled augmentation, and Stance Contrastive Learning (SCL). On the Implicit Hate Corpus, stance variant augmentation reduces counter speech false positives by 60{\%}, and SCL further improves cross domain F1 by up to 6.5 points on top of the augmented signal. On SBIC, where use mention conflation is less prominent in the training labels, SCL maintains a low counter speech false positive rate. Semantic similarity pairing on the same augmented data yields no cross domain gain over the model trained without contrastive learning (0.623 vs. 0.625), which identifies the pair construction signal rather than contrastive learning itself as the determining factor."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lin-etal-2026-learning">
<titleInfo>
<title>Learning What Is Said, Not How: Stance-Contrastive Learning for Generalizable Hate Speech Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yucheng</namePart>
<namePart type="family">Lin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dan</namePart>
<namePart type="family">Kienast</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiaojiao</namePart>
<namePart type="family">Jiang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yue</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruifeng</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ayu</namePart>
<namePart type="family">Purwarianti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tong</namePart>
<namePart type="family">Rong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jun</namePart>
<namePart type="family">Suzuki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Hengqin, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-439-2</identifier>
</relatedItem>
<abstract>Hate speech classifiers often misclassify counter speech, including condemnation and quotation of hateful content, as hateful because they fail to distinguish propositional content from speaker stance. We identify this issue as content stance conflation and address it as a representation learning problem. We learn stance invariant representations of the same proposition by constructing contrastive pairs that share content but differ in stance. We implement this idea through factorized annotation, stance controlled augmentation, and Stance Contrastive Learning (SCL). On the Implicit Hate Corpus, stance variant augmentation reduces counter speech false positives by 60%, and SCL further improves cross domain F1 by up to 6.5 points on top of the augmented signal. On SBIC, where use mention conflation is less prominent in the training labels, SCL maintains a low counter speech false positive rate. Semantic similarity pairing on the same augmented data yields no cross domain gain over the model trained without contrastive learning (0.623 vs. 0.625), which identifies the pair construction signal rather than contrastive learning itself as the determining factor.</abstract>
<identifier type="citekey">lin-etal-2026-learning</identifier>
<location>
<url>https://aclanthology.org/2026.aacl-long.61/</url>
</location>
<part>
<date>2026-11</date>
<extent unit="page">
<start>1301</start>
<end>1318</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Learning What Is Said, Not How: Stance-Contrastive Learning for Generalizable Hate Speech Detection
%A Lin, Yucheng
%A Kienast, Dan
%A Jiang, Jiaojiao
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F lin-etal-2026-learning
%X Hate speech classifiers often misclassify counter speech, including condemnation and quotation of hateful content, as hateful because they fail to distinguish propositional content from speaker stance. We identify this issue as content stance conflation and address it as a representation learning problem. We learn stance invariant representations of the same proposition by constructing contrastive pairs that share content but differ in stance. We implement this idea through factorized annotation, stance controlled augmentation, and Stance Contrastive Learning (SCL). On the Implicit Hate Corpus, stance variant augmentation reduces counter speech false positives by 60%, and SCL further improves cross domain F1 by up to 6.5 points on top of the augmented signal. On SBIC, where use mention conflation is less prominent in the training labels, SCL maintains a low counter speech false positive rate. Semantic similarity pairing on the same augmented data yields no cross domain gain over the model trained without contrastive learning (0.623 vs. 0.625), which identifies the pair construction signal rather than contrastive learning itself as the determining factor.
%U https://aclanthology.org/2026.aacl-long.61/
%P 1301-1318
Markdown (Informal)
[Learning What Is Said, Not How: Stance-Contrastive Learning for Generalizable Hate Speech Detection](https://aclanthology.org/2026.aacl-long.61/) (Lin et al., AACL-IJCNLP 2026)
ACL