@inproceedings{reddy-renjit-2026-hate,
title = "Does Hate Transfer? Cross-Lingual Generalisation of Offensive Content Detection Across {I}ndic Languages",
author = "Reddy, Purandhar M. and
Renjit, Sara",
editor = "Mitkov, Ruslan and
Mu{\~n}oz, Rafael and
Lloret, Elena and
Ranasinghe, Tharindu and
Estevanell-Valladares, Ernesto L. and
Lamsiyah, Salima and
Montoyo, Andr{\'e}s and
Ezzini, Saad",
booktitle = "Proceedings of the Second International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
month = jun,
year = "2026",
address = "Alicante, Spain",
publisher = "Department of Languages and Information Systems, University of Alicante",
url = "https://aclanthology.org/2026.nlpaics-1.24/",
pages = "228--231",
abstract = "A common assumption in low-resource NLP is that cross-lingual transfer from a related language can substitute for target-language annotation when labelled data is scarce. We test this assumption for offensive content detection across five Indic languages by evaluating all twenty directed transfer pairs from a LLaMA3.1-8B model fine-tuned with Low-Rank Adaptation (LoRA) on the MACD benchmark. Only three of twenty pairs achieve tolerable transfer loss below 15{\%}, all involving Malayalam as the source language. Telugu is the hardest transfer target (average loss 33.8{\%}), while Malayalam is the most transferable source (average loss 16.8{\%}). Confusion-matrix analysis reveals two distinct failure modes: Tamiland Kannada-trained models are conservative under-flaggers that miss 73{--}82{\%} of offensive content with near-zero false alarms, while Malayalam-trained models are aggressive flaggers that miss far less (39{\%}) but over-flag at 21{\%}. These patterns do not follow typological structure: a Spearman correlation between URIEL typological similarity and transfer F1 yields {\ensuremath{\rho}} = {\ensuremath{-}}0.254 (p = 0.281), failing to conf irm the typological hypothesis. Our results indicate that cross-lingual shortcuts are unreliable for this task and that language-specific annotation cannot be avoided by appealing to linguistic family membership."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="reddy-renjit-2026-hate">
<titleInfo>
<title>Does Hate Transfer? Cross-Lingual Generalisation of Offensive Content Detection Across Indic Languages</title>
</titleInfo>
<name type="personal">
<namePart type="given">Purandhar</namePart>
<namePart type="given">M</namePart>
<namePart type="family">Reddy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sara</namePart>
<namePart type="family">Renjit</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Second International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ruslan</namePart>
<namePart type="family">Mitkov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rafael</namePart>
<namePart type="family">Muñoz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elena</namePart>
<namePart type="family">Lloret</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tharindu</namePart>
<namePart type="family">Ranasinghe</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ernesto</namePart>
<namePart type="given">L</namePart>
<namePart type="family">Estevanell-Valladares</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Salima</namePart>
<namePart type="family">Lamsiyah</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andrés</namePart>
<namePart type="family">Montoyo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Saad</namePart>
<namePart type="family">Ezzini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Department of Languages and Information Systems, University of Alicante</publisher>
<place>
<placeTerm type="text">Alicante, Spain</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>A common assumption in low-resource NLP is that cross-lingual transfer from a related language can substitute for target-language annotation when labelled data is scarce. We test this assumption for offensive content detection across five Indic languages by evaluating all twenty directed transfer pairs from a LLaMA3.1-8B model fine-tuned with Low-Rank Adaptation (LoRA) on the MACD benchmark. Only three of twenty pairs achieve tolerable transfer loss below 15%, all involving Malayalam as the source language. Telugu is the hardest transfer target (average loss 33.8%), while Malayalam is the most transferable source (average loss 16.8%). Confusion-matrix analysis reveals two distinct failure modes: Tamiland Kannada-trained models are conservative under-flaggers that miss 73–82% of offensive content with near-zero false alarms, while Malayalam-trained models are aggressive flaggers that miss far less (39%) but over-flag at 21%. These patterns do not follow typological structure: a Spearman correlation between URIEL typological similarity and transfer F1 yields \ensuremathρ = \ensuremath-0.254 (p = 0.281), failing to conf irm the typological hypothesis. Our results indicate that cross-lingual shortcuts are unreliable for this task and that language-specific annotation cannot be avoided by appealing to linguistic family membership.</abstract>
<identifier type="citekey">reddy-renjit-2026-hate</identifier>
<location>
<url>https://aclanthology.org/2026.nlpaics-1.24/</url>
</location>
<part>
<date>2026-06</date>
<extent unit="page">
<start>228</start>
<end>231</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Does Hate Transfer? Cross-Lingual Generalisation of Offensive Content Detection Across Indic Languages
%A Reddy, Purandhar M.
%A Renjit, Sara
%Y Mitkov, Ruslan
%Y Muñoz, Rafael
%Y Lloret, Elena
%Y Ranasinghe, Tharindu
%Y Estevanell-Valladares, Ernesto L.
%Y Lamsiyah, Salima
%Y Montoyo, Andrés
%Y Ezzini, Saad
%S Proceedings of the Second International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security
%D 2026
%8 June
%I Department of Languages and Information Systems, University of Alicante
%C Alicante, Spain
%F reddy-renjit-2026-hate
%X A common assumption in low-resource NLP is that cross-lingual transfer from a related language can substitute for target-language annotation when labelled data is scarce. We test this assumption for offensive content detection across five Indic languages by evaluating all twenty directed transfer pairs from a LLaMA3.1-8B model fine-tuned with Low-Rank Adaptation (LoRA) on the MACD benchmark. Only three of twenty pairs achieve tolerable transfer loss below 15%, all involving Malayalam as the source language. Telugu is the hardest transfer target (average loss 33.8%), while Malayalam is the most transferable source (average loss 16.8%). Confusion-matrix analysis reveals two distinct failure modes: Tamiland Kannada-trained models are conservative under-flaggers that miss 73–82% of offensive content with near-zero false alarms, while Malayalam-trained models are aggressive flaggers that miss far less (39%) but over-flag at 21%. These patterns do not follow typological structure: a Spearman correlation between URIEL typological similarity and transfer F1 yields \ensuremathρ = \ensuremath-0.254 (p = 0.281), failing to conf irm the typological hypothesis. Our results indicate that cross-lingual shortcuts are unreliable for this task and that language-specific annotation cannot be avoided by appealing to linguistic family membership.
%U https://aclanthology.org/2026.nlpaics-1.24/
%P 228-231
Markdown (Informal)
[Does Hate Transfer? Cross-Lingual Generalisation of Offensive Content Detection Across Indic Languages](https://aclanthology.org/2026.nlpaics-1.24/) (Reddy & Renjit, NLPAICS 2026)
ACL