@inproceedings{zhou-etal-2026-teleai,
title = "{T}ele{AI} at {S}em{E}val-2026 Task 3: Large Language Models for Dimensional Aspect-Based Sentiment Analysis",
author = "Zhou, Yan and
Wang, Wangshicheng and
Wang, Shiquan and
Bao, Mengjiao and
Fang, Ruiyu and
Song, Shuangyong and
Li, Yongxiang and
Li, Xuelong",
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-1.233/",
pages = "1846--1852",
ISBN = "979-8-89176-414-9",
abstract = "This paper describes TeleAI{'}s system for SemEval-2026 Task 3, Track A, Subtask 1 (DimASR), which focuses on predicting continuous Valence-Arousal (VA) scores for specific aspects in text. We frame this task as an end-to-end regression problem and propose a robust framework utilizing Qwen2.5-7B as the feature extraction backbone, combined with parameter-efficient fine-tuning via LoRA. To enhance model generalization and mitigate domain shifts, we primarily leverage multilingual and multi-domain mixed training. Furthermore, our system integrates several optimization and robustness techniques to stabilize continuous score prediction, including R-Drop-style consistency regularization, embedding-level PGD adversarial training, Smooth L1 (Huber) loss, sigmoid-based output interval mapping, and post-hoc linear calibration. Our comprehensive ablations demonstrate that the combination of joint training and robustness regularizations substantially reduces the official evaluation metric, {\$}RMSE{\{}VA{\}}{\$}. The proposed system achieves highly competitive performance across multiple language and domain settings, demonstrating the efficacy of applying lightweight LLM adaptation for dimensional aspect-based sentiment analysis."
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<abstract>This paper describes TeleAI’s system for SemEval-2026 Task 3, Track A, Subtask 1 (DimASR), which focuses on predicting continuous Valence-Arousal (VA) scores for specific aspects in text. We frame this task as an end-to-end regression problem and propose a robust framework utilizing Qwen2.5-7B as the feature extraction backbone, combined with parameter-efficient fine-tuning via LoRA. To enhance model generalization and mitigate domain shifts, we primarily leverage multilingual and multi-domain mixed training. Furthermore, our system integrates several optimization and robustness techniques to stabilize continuous score prediction, including R-Drop-style consistency regularization, embedding-level PGD adversarial training, Smooth L1 (Huber) loss, sigmoid-based output interval mapping, and post-hoc linear calibration. Our comprehensive ablations demonstrate that the combination of joint training and robustness regularizations substantially reduces the official evaluation metric, $RMSE{VA}$. The proposed system achieves highly competitive performance across multiple language and domain settings, demonstrating the efficacy of applying lightweight LLM adaptation for dimensional aspect-based sentiment analysis.</abstract>
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%0 Conference Proceedings
%T TeleAI at SemEval-2026 Task 3: Large Language Models for Dimensional Aspect-Based Sentiment Analysis
%A Zhou, Yan
%A Wang, Wangshicheng
%A Wang, Shiquan
%A Bao, Mengjiao
%A Fang, Ruiyu
%A Song, Shuangyong
%A Li, Yongxiang
%A Li, Xuelong
%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 zhou-etal-2026-teleai
%X This paper describes TeleAI’s system for SemEval-2026 Task 3, Track A, Subtask 1 (DimASR), which focuses on predicting continuous Valence-Arousal (VA) scores for specific aspects in text. We frame this task as an end-to-end regression problem and propose a robust framework utilizing Qwen2.5-7B as the feature extraction backbone, combined with parameter-efficient fine-tuning via LoRA. To enhance model generalization and mitigate domain shifts, we primarily leverage multilingual and multi-domain mixed training. Furthermore, our system integrates several optimization and robustness techniques to stabilize continuous score prediction, including R-Drop-style consistency regularization, embedding-level PGD adversarial training, Smooth L1 (Huber) loss, sigmoid-based output interval mapping, and post-hoc linear calibration. Our comprehensive ablations demonstrate that the combination of joint training and robustness regularizations substantially reduces the official evaluation metric, $RMSE{VA}$. The proposed system achieves highly competitive performance across multiple language and domain settings, demonstrating the efficacy of applying lightweight LLM adaptation for dimensional aspect-based sentiment analysis.
%U https://aclanthology.org/2026.semeval-1.233/
%P 1846-1852Markdown (Informal)
[TeleAI at SemEval-2026 Task 3: Large Language Models for Dimensional Aspect-Based Sentiment Analysis](https://aclanthology.org/2026.semeval-1.233/) (Zhou et al., SemEval 2026)
ACL
- Yan Zhou, Wangshicheng Wang, Shiquan Wang, Mengjiao Bao, Ruiyu Fang, Shuangyong Song, Yongxiang Li, and Xuelong Li. 2026. TeleAI at SemEval-2026 Task 3: Large Language Models for Dimensional Aspect-Based Sentiment Analysis. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 1846–1852, San Diego, California, USA. Association for Computational Linguistics.