@inproceedings{poh-etal-2026-pragmatic,
title = "Does Pragmatic Transfer Help Euphemism Detection? Evidence from Target-Span Marking",
author = "Poh, Whitney and
Takahashi, Noriko and
Lee, Patrick and
Sammartino, Julia and
Barak, Libby and
Peng, Jing and
Feldman, Anna",
editor = "Sedoc, Jo{\~a}o and
Shu, Raphael and
Drozd, Aleksandr and
Akula, Arjun and
Rogers, Anna and
Giorgi, Salvatore and
Benton, Adrian",
booktitle = "Proceedings of the Seventh Workshop on Insights from Negative Results in {NLP}",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.insights-1.3/",
pages = "19--32",
ISBN = "979-8-89176-480-4",
abstract = "Euphemism detection is often motivated as a pragmatic task: deciding whether a potentially euphemistic term (PET) is used euphemistically depends on context, communicative goals, politeness, topic sensitivity, and sentiment. A natural prediction is that auxiliary supervision from pragmatically related tasks should improve euphemism detection when target supervision is limited. We test this prediction for PET-style classification using XLM-R and mBERT in a feature-based transfer setting: auxiliary-tuned encoders are frozen and probed for euphemism detection. We compare sentence-level pragmatic proxy tasks with expression-level nonliteral tasks and with target inputs that either mark or omit the PET span. The results do not support a simple pragmatic-transfer account. Auxiliary transfer is unreliable, often reducing XLM-R performance and helping mBERT only inconsistently. The largest observed gains come from direct euphemism fine-tuning when the target PET is explicitly marked. The practical implication is to preserve target-span information and report source{--}target compatibility in transfer experiments."
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<abstract>Euphemism detection is often motivated as a pragmatic task: deciding whether a potentially euphemistic term (PET) is used euphemistically depends on context, communicative goals, politeness, topic sensitivity, and sentiment. A natural prediction is that auxiliary supervision from pragmatically related tasks should improve euphemism detection when target supervision is limited. We test this prediction for PET-style classification using XLM-R and mBERT in a feature-based transfer setting: auxiliary-tuned encoders are frozen and probed for euphemism detection. We compare sentence-level pragmatic proxy tasks with expression-level nonliteral tasks and with target inputs that either mark or omit the PET span. The results do not support a simple pragmatic-transfer account. Auxiliary transfer is unreliable, often reducing XLM-R performance and helping mBERT only inconsistently. The largest observed gains come from direct euphemism fine-tuning when the target PET is explicitly marked. The practical implication is to preserve target-span information and report source–target compatibility in transfer experiments.</abstract>
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%0 Conference Proceedings
%T Does Pragmatic Transfer Help Euphemism Detection? Evidence from Target-Span Marking
%A Poh, Whitney
%A Takahashi, Noriko
%A Lee, Patrick
%A Sammartino, Julia
%A Barak, Libby
%A Peng, Jing
%A Feldman, Anna
%Y Sedoc, João
%Y Shu, Raphael
%Y Drozd, Aleksandr
%Y Akula, Arjun
%Y Rogers, Anna
%Y Giorgi, Salvatore
%Y Benton, Adrian
%S Proceedings of the Seventh Workshop on Insights from Negative Results in NLP
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-480-4
%F poh-etal-2026-pragmatic
%X Euphemism detection is often motivated as a pragmatic task: deciding whether a potentially euphemistic term (PET) is used euphemistically depends on context, communicative goals, politeness, topic sensitivity, and sentiment. A natural prediction is that auxiliary supervision from pragmatically related tasks should improve euphemism detection when target supervision is limited. We test this prediction for PET-style classification using XLM-R and mBERT in a feature-based transfer setting: auxiliary-tuned encoders are frozen and probed for euphemism detection. We compare sentence-level pragmatic proxy tasks with expression-level nonliteral tasks and with target inputs that either mark or omit the PET span. The results do not support a simple pragmatic-transfer account. Auxiliary transfer is unreliable, often reducing XLM-R performance and helping mBERT only inconsistently. The largest observed gains come from direct euphemism fine-tuning when the target PET is explicitly marked. The practical implication is to preserve target-span information and report source–target compatibility in transfer experiments.
%U https://aclanthology.org/2026.insights-1.3/
%P 19-32
Markdown (Informal)
[Does Pragmatic Transfer Help Euphemism Detection? Evidence from Target-Span Marking](https://aclanthology.org/2026.insights-1.3/) (Poh et al., insights 2026)
ACL