@inproceedings{lin-etal-2026-ntnu,
title = "{NTNU}-{SMIL} at {S}em{E}val-2026 Task 3: Logistic-Loss Regression with Same-Language Transfer for Valence{--}Arousal Stance Prediction in Dimensional Stance Analysis ({D}im{S}tance)",
author = "Lin, Siang-Ting and
Lo, Tien-Hong and
Sun, Yun-Ting and
Guo, Jhih-Rong and
Hao, Tung-Yen and
Tsai, Fong-Chun and
Chen, Berlin",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.3/",
pages = "15--21",
ISBN = "979-8-89176-414-9",
abstract = "We propose NTNU-SMIL{'}s system for SemEval-2026 Task 3 Track B Subtask 1 Dimensional Stance Analysis (DimStance). Our approach models target-conditioned valence{--}arousal regression using sentence-pair encoding, dual regression heads, and a logistic-loss regression formulation. For English and Chinese, we further leverage same-language transfer from Track A and apply lightweight out-of-fold calibration with multi-seed ensembling to reduce cross-lingual scale mismatch. Post-hoc analysis shows that same-language transfer and logistic-loss regression are the main drivers of performance gains, while arousal variance collapse remains a challenge in low-resource settings such as Swahili."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lin-etal-2026-ntnu">
<titleInfo>
<title>NTNU-SMIL at SemEval-2026 Task 3: Logistic-Loss Regression with Same-Language Transfer for Valence–Arousal Stance Prediction in Dimensional Stance Analysis (DimStance)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Siang-Ting</namePart>
<namePart type="family">Lin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tien-Hong</namePart>
<namePart type="family">Lo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yun-Ting</namePart>
<namePart type="family">Sun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jhih-Rong</namePart>
<namePart type="family">Guo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tung-Yen</namePart>
<namePart type="family">Hao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fong-Chun</namePart>
<namePart type="family">Tsai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Berlin</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Kochmar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Debanjan</namePart>
<namePart type="family">Ghosh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kai</namePart>
<namePart type="family">North</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mamoru</namePart>
<namePart type="family">Komachi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-414-9</identifier>
</relatedItem>
<abstract>We propose NTNU-SMIL’s system for SemEval-2026 Task 3 Track B Subtask 1 Dimensional Stance Analysis (DimStance). Our approach models target-conditioned valence–arousal regression using sentence-pair encoding, dual regression heads, and a logistic-loss regression formulation. For English and Chinese, we further leverage same-language transfer from Track A and apply lightweight out-of-fold calibration with multi-seed ensembling to reduce cross-lingual scale mismatch. Post-hoc analysis shows that same-language transfer and logistic-loss regression are the main drivers of performance gains, while arousal variance collapse remains a challenge in low-resource settings such as Swahili.</abstract>
<identifier type="citekey">lin-etal-2026-ntnu</identifier>
<location>
<url>https://aclanthology.org/2026.semeval-1.3/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>15</start>
<end>21</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T NTNU-SMIL at SemEval-2026 Task 3: Logistic-Loss Regression with Same-Language Transfer for Valence–Arousal Stance Prediction in Dimensional Stance Analysis (DimStance)
%A Lin, Siang-Ting
%A Lo, Tien-Hong
%A Sun, Yun-Ting
%A Guo, Jhih-Rong
%A Hao, Tung-Yen
%A Tsai, Fong-Chun
%A Chen, Berlin
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F lin-etal-2026-ntnu
%X We propose NTNU-SMIL’s system for SemEval-2026 Task 3 Track B Subtask 1 Dimensional Stance Analysis (DimStance). Our approach models target-conditioned valence–arousal regression using sentence-pair encoding, dual regression heads, and a logistic-loss regression formulation. For English and Chinese, we further leverage same-language transfer from Track A and apply lightweight out-of-fold calibration with multi-seed ensembling to reduce cross-lingual scale mismatch. Post-hoc analysis shows that same-language transfer and logistic-loss regression are the main drivers of performance gains, while arousal variance collapse remains a challenge in low-resource settings such as Swahili.
%U https://aclanthology.org/2026.semeval-1.3/
%P 15-21
Markdown (Informal)
[NTNU-SMIL at SemEval-2026 Task 3: Logistic-Loss Regression with Same-Language Transfer for Valence–Arousal Stance Prediction in Dimensional Stance Analysis (DimStance)](https://aclanthology.org/2026.semeval-1.3/) (Lin et al., SemEval 2026)
ACL
- Siang-Ting Lin, Tien-Hong Lo, Yun-Ting Sun, Jhih-Rong Guo, Tung-Yen Hao, Fong-Chun Tsai, and Berlin Chen. 2026. NTNU-SMIL at SemEval-2026 Task 3: Logistic-Loss Regression with Same-Language Transfer for Valence–Arousal Stance Prediction in Dimensional Stance Analysis (DimStance). In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 15–21, San Diego, California, USA. Association for Computational Linguistics.