AILS-NTUA at SemEval-2026 Task 3: Efficient Dimensional Aspect-Based Sentiment Analysis

Stavros Gazetas; Giorgos Filandrianos; Maria Lymperaiou; Paraskevi Tzouveli; Athanasios Voulodimos; Giorgos Stamou

cs.CLMar 5, 2026

AILS-NTUA at SemEval-2026 Task 3: Efficient Dimensional Aspect-Based Sentiment Analysis

Authors: Stavros Gazetas, Giorgos Filandrianos, Maria Lymperaiou, Paraskevi Tzouveli, Athanasios Voulodimos, Giorgos Stamou

Organizations: School of Electrical and Computer Engineering, AILS Laboratory National Technical University of Athens

Abstract

In this paper, we present AILS-NTUA system for Track-A of SemEval-2026 Task 3 on Dimensional Aspect-Based Sentiment Analysis (DimABSA), which encompasses three complementary problems: Dimensional Aspect Sentiment Regression (DimASR), Dimensional Aspect Sentiment Triplet Extraction (DimASTE), and Dimensional Aspect Sentiment Quadruplet Prediction (DimASQP) within a multilingual and multi-domain framework. Our methodology combines fine-tuning of language-appropriate encoder backbones for continuous aspect-level sentiment prediction with language-specific instruction tuning of large language models using LoRA for structured triplet and quadruplet extraction. This unified yet task-adaptive design emphasizes parameter-efficient specialization across languages and domains, enabling reduced training and inference requirements while maintaining strong effectiveness. Empirical results demonstrate that the proposed models achieve competitive performance and consistently surpass the provided baselines across most evaluation settings.

Figures & tables

Lang.	Domain	Dev			Test
		RMSE VA $\downarrow$	PCC V $\uparrow$	PCC A $\uparrow$	RMSE VA $\downarrow$	PCC V $\uparrow$	PCC A $\uparrow$
ENG	Laptop	$1.0166$	$0.9174$	$0.5768$	$1.4401$	$0.8520$	$0.5073$
ENG	Restaurant	$0.9208$	$0.9295$	$0.6882$	$1.3933$	$0.8757$	$0.6105$
ZHO	Finance	$0.5438$	$0.8241$	$0.5981$	$0.5425$	$0.8327$	$0.6436$
	Laptop	$0.7443$	$0.8863$	$0.7042$	$0.7457$	$0.8738$	$0.7133$
	Restaurant	$0.8007$	$0.8419$	$0.6748$	$1.0023$	$0.8258$	$0.5852$

Table 1: DimASR results per language and domain.

Lang.	Model	Domain	cF1 $\uparrow$
			Dev	Test
ENG	Llama 3.1 8B	Laptop	$0.5962$	$0.5311$
ENG	Llama 3.1 8B	Restaurant	$0.7668$	$0.6518$
ZHO	Qwen 2.5 7B	Laptop	$0.4193$	$0.4646$
ZHO	Qwen 2.5 7B	Restaurant	$0.5916$	$0.5042$
JPN	Qwen 2.5 14B	Hotel	$0.4879$	$0.5021$

Table 2: DimASTE results per language and domain.

Lang.	Model	Domain	cF1 $\uparrow$
			Dev	Test
ENG	Llama 3.1 8B	Laptop	$0.3556$	$0.2694$
ENG	Llama 3.1 8B	Restaurant	$0.7560$	$0.5988$
ZHO	Qwen 2.5 7B	Laptop	$0.3662$	$0.3703$
ZHO	Qwen 2.5 7B	Restaurant	$0.5665$	$0.4544$
JPN	Qwen 2.5 14B	Hotel	$0.4218$	$0.3747$

Table 3: DimASQP results per language and domain.

Lang.	Translate+Train	ENG-Model
ZHO	$0.36$	$0.26$
RUS	$0.31$	$0.31$
UKR	$0.24$	$0.25$
TAT	$0.20$	$0.22$

Table 4: Restaurant DimASQP (cF1), Dev set, Llama 3.1 8B: translation training vs. English-trained model.

Appendix figures & tables12 assets

Supplementary material from the paper’s appendix.

Appendix

Subtask	Example (JSON)
DimASR	{"ID": "rest26_aspect_va_dev_1", "Text": "Great diner food and breakfast is served all day", "Aspect_VA": [{"Aspect": "diner food", "VA": "7.25#6.75"}, {"Aspect": "breakfast", "VA": "7.25#6.75"}]}
DimASTE	{"ID": "rest26_aste_dev_2", "Text": "Customer service was fantastic and food was awesome", "Triplet": [{"Aspect": "Customer service", "Opinion": "fantastic", "VA": "7.33#7.33"}, {"Aspect": "food", "Opinion": "awesome", "VA": "7.67#7.67"}]}
DimASQP	{"ID": "rest26_asqp_dev_1", "Text": "Food and coffee are great", "Quadruplet": [{"Aspect": "Food", "Category": "FOOD#QUALITY", "Opinion": "great", "VA": "7.67#7.83"}, {"Aspect": "coffee", "Category": "DRINKS#QUALITY", "Opinion": "great", "VA": "7.67#8.00"}]}

Appendix

Table 5: Example JSON format for each Subtask.

Subtask	Lang.	Domain	Zero-shot		One-shot		Supervised Fine-Tuning
			GPT-5 mini	Kimi K2 Thinking	GPT-5 mini	Kimi K2 Thinking	Qwen 3 14B	Mistral 3 14B	Llama 3.3 70B	GPT-OSS 120B
DimASR	ENG	Restaurant	$2.9490$	$2.3432$	$2.3926$	$2.1461$	$2.6427$	$2.6316$	$2.5244$	$1.4605$
	ENG	Laptop	$3.2115$	$2.6546$	$2.5637$	$2.1893$	$2.8089$	$2.6258$	$2.7354$	$1.5269$
	JPN	Hotel	$3.1406$	$2.3294$	$2.1607$	$1.7553$	$2.2906$	$2.2999$	$2.6255$	$0.7188$
	JPN	Finance	$2.6760$	$2.3379$	$1.9243$	$1.6396$	$1.8964$	$2.0700$	$2.4191$	$1.0188$
	RUS	Restaurant	$2.5447$	$2.0630$	$2.0390$	$1.7768$	$2.1528$	$2.3617$	$2.5089$	$1.4775$

Appendix

Table 6: Combined Benchmark paper’s results across subtasks. DimASR values are RMSE VA ; DimASTE and DimASQP values are cF1.

Language	Model family	Model ID
ENG	DeBERTa	yangheng/deberta-v3-base-absa-v1.1 8 8 8 yangheng/deberta-v3-base-absa-v1.1
Japanese	DeBERTa	ku-nlp/deberta-v3-base-japanese 9 9 9 ku-nlp/deberta-v3-base-japanese
Chinese	RoBERTa	hfl/chinese-roberta-wwm-ext 10 10 10 hfl/chinese-roberta-wwm-ext
Russian	BERT-style	DeepPavlov/rubert-base-cased 11 11 11 DeepPavlov/rubert-base-cased
Ukrainian	XLM-R	FacebookAI/xlm-roberta-base 12 12 12 FacebookAI/xlm-roberta-base
Tatar	XLM-R	FacebookAI/xlm-roberta-base 13 13 13 FacebookAI/xlm-roberta-base

Appendix

Table 7: Language-specific encoder backbones used for DimASR.

Hyperparameter	Value
Learning rate ( $\eta$ )	$2e-5$
Batch size	16
Dropout	0.3
Warmup ratio	0.1
Early stopping patience	5
Max sequence length	128

Appendix

Table 8: Training hyperparameters for the DimASR models.

Hyperparameter	Value
Train epochs ( $E$ )	1
Per-device batch size	2
Gradient accumulation steps	4
Effective batch (per device)	$2\times 4=8$
Learning rate ( $\eta$ )	$2e-4$
Weight decay	$1\times 10^{-4}$

Appendix

Table 9: LLM fine-tuning configuration (4-bit loading + LoRA adapters) for DimASTE & DimASQP.

Lang.	Domain	Dev cF1 $\uparrow$
		Zero-shot	Few-shot
ENG	Laptop	$0.48$	$0.59$
ENG	Restaurant	$0.53$	$0.59$
ZHO	Laptop	$0.23$	$0.27$
ZHO	Restaurant	$0.37$	$0.43$
JPN	Hotel	$0.17$	$0.19$

Appendix

Table 10: DimASTE Dev set results in zero-shot and few-shot prompting (cF1) using Qwen 2.5 14B Instruct.

Lang.	Domain	Dev cF1 $\uparrow$
		Zero-shot	Few-shot
ENG	Laptop	$0.22$	$0.32$
ENG	Restaurant	$0.42$	$0.51$
ZHO	Laptop	$0.14$	$0.18$
ZHO	Restaurant	$0.23$	$0.31$
JPN	Hotel	$0.06$	$0.11$

Appendix

Table 11: DimASQP Dev set results in zero-shot and few-shot prompting (cF1) using Qwen 2.5 14B Instruct.

Lang.	Model	Domain	Dev cF1 $\uparrow$
ENG	Llama 3.1 70B	Laptop	$0.3373$
ENG	Llama 3.1 70B	Restaurant	$0.7441$
ZHO	Qwen 2.5 32B	Laptop	$0.3119$
	Qwen 2.5 32B	Restaurant	$0.5220$
	Qwen 2.5 72B	Laptop	$0.3675$
	Qwen 2.5 72B	Restaurant	$0.5508$

Appendix

Table 12: DimASQP development set results (cF1) after lora-tuning larger LLMs.

Figure 1: DimASR dataset statistics across splits: number of reviews (left) and aspects per review (right).

Figure 2: DimASTE and DimASQP dataset statistics across splits: number of reviews (left) and quadruplets per review (right).

Figure 3: DimASTE and DimASQP NULL analysis on Train set: NULL rate (left) and composition of NULL cases (right).

Figure 4: DimASTE and DimASQP distribution shift measured by PSI between Train and Dev/Test across features (review length, quadruplets/review, and category).

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This paper describes our system to SemEval-2026 Task 3 Track A Subtask 1 on Dimensional Aspect Sentiment Regression (DimASR). We propose a lightweight and resource-efficient system built entirely on multilingual pre-trained encoders, without relying on LLMs or external corpora. We adopt joint multilingual and multi-domain training to facilitate cross-lingual transfer and alleviate data sparsity, introduce a bounded regression transformation that improves training stability while constraining predictions within the valid range, and employ an adaptive ensemble strategy via subset search to reduce prediction variance. Experimental results demonstrate that our system achieves strong and consistent performance, ranking 1st on zho-res, 2nd on zho-lap, and 3rd on jpn-hot, with all remaining datasets placed within the top half of participating teams.

Abstract

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