TY - GEN
T1 - When Semantic Overlap Is Not Enough
T2 - 2nd Workshop on Natural Language Processing for Turkic Languages, SIGTURK 2026
AU - Biyik, Hasan Can
AU - Barak, Libby
AU - Peng, Jing
AU - Feldman, Anna
N1 - Publisher Copyright:
© 2026 Association for Computational Linguistics.
PY - 2026
Y1 - 2026
N2 - Euphemisms substitute socially sensitive expressions, often softening or reframing meaning, and their reliance on cultural and pragmatic context complicates modeling across languages. In this study, we investigate how cross-lingual equivalence influences transfer in multilingual euphemism detection. We categorize Potentially Euphemistic Terms (PETs) in Turkish and English into Overlapping (OPETs) and Non-Overlapping (NOPETs) subsets based on their functional, pragmatic, and semantic alignment. Our findings reveal a transfer asymmetry: semantic overlap is insufficient to guarantee positive transfer, particularly in low-resource Turkish-to-English direction, where performance can degrade even for overlapping euphemisms, and in some cases, improve under NOPET-based training. Differences in label distribution help explain these counterintuitive results. Category-level analysis suggests that transfer may be influenced by domain-specific alignment, though evidence is limited by sparsity.
AB - Euphemisms substitute socially sensitive expressions, often softening or reframing meaning, and their reliance on cultural and pragmatic context complicates modeling across languages. In this study, we investigate how cross-lingual equivalence influences transfer in multilingual euphemism detection. We categorize Potentially Euphemistic Terms (PETs) in Turkish and English into Overlapping (OPETs) and Non-Overlapping (NOPETs) subsets based on their functional, pragmatic, and semantic alignment. Our findings reveal a transfer asymmetry: semantic overlap is insufficient to guarantee positive transfer, particularly in low-resource Turkish-to-English direction, where performance can degrade even for overlapping euphemisms, and in some cases, improve under NOPET-based training. Differences in label distribution help explain these counterintuitive results. Category-level analysis suggests that transfer may be influenced by domain-specific alignment, though evidence is limited by sparsity.
UR - https://www.scopus.com/pages/publications/105042182866
U2 - 10.18653/v1/2026.sigturk-1.11
DO - 10.18653/v1/2026.sigturk-1.11
M3 - Conference contribution
AN - SCOPUS:105042182866
T3 - SIGTURK 2026 - 2nd Workshop on Natural Language Processing for Turkic Languages, Proceedings of the Workshop
SP - 113
EP - 125
BT - SIGTURK 2026 - 2nd Workshop on Natural Language Processing for Turkic Languages, Proceedings of the Workshop
A2 - Oflazer, Kemal
A2 - Koksal, Abdullatif
A2 - Varol, Onur
PB - Association for Computational Linguistics (ACL)
Y2 - 29 March 2026
ER -