nchellwig at SemEval-2026 Task 3: Self-Consistent Structured Generation (SCSG) for Dimensional Aspect-Based Sentiment Analysis using Large Language Models

Nils Constantin Hellwig; Jakob Fehle; Udo Kruschwitz; Christian Wolff

cs.CLMar 2, 2026

nchellwig at SemEval-2026 Task 3: Self-Consistent Structured Generation (SCSG) for Dimensional Aspect-Based Sentiment Analysis using Large Language Models

Authors: Nils Constantin Hellwig, Jakob Fehle, Udo Kruschwitz, Christian Wolff

Organizations: Media Informatics Group, University of Regensburg, Regensburg, Germany · Information Science Group, University of Regensburg, Regensburg, Germany

Abstract

We present Self-Consistent Structured Generation (SCSG) for Dimensional Aspect-Based Sentiment Analysis in SemEval-2026 Task 3 (Track A). SCSG enhances prediction reliability by executing a LoRA-adapted large language model multiple times per instance, retaining only tuples that achieve a majority consensus across runs. To mitigate the computational overhead of multiple forward passes, we leverage vLLM's PagedAttention mechanism for efficient key--value cache reuse. Evaluation across 6 languages and 8 language--domain combinations demonstrates that self-consistency with 15 executions yields statistically significant improvements over single-inference prompting, with our system (leveraging Gemma 3) ranking in the top seven across all settings, achieving second place on three out of four English subsets and first place on Tatar-Restaurant for DimASTE.

Figures & tables

Input Text
Decor is nice though service can be spotty.
DimASQP Output (Quadruplets)
$\langle$ Decor , ambience#general , nice , $v{=}7.00,a{=}7.17$ $\rangle$
$\langle$ service , service#general , spotty , $v{=}7.00,a{=}7.00$ $\rangle$
DimASTE Output (Triplets)
$\langle$ Decor , nice , $v{=}7.00,a{=}7.17$ $\rangle$

Table 1: Example outputs for DimASQP and DimASTE tasks. DimASQP extracts quadruplets containing aspect term, aspect category, opinion term, and valence-arousal (

v,a

) dimensions, where

v,a\in[1,9]

, while DimASTE extracts triplets omitting the aspect category.

Table 2: Performance on the test set for DimASTE and DimASQP : Comparison of vanilla prompting (Baseline) and self-consistency (SC) with 5, 10, or 15 prompt executions. Results show continuous-level precision (cPrec), recall (cRec), and F1-score (cF1) in %. Bold values indicate the best performance for each language–domain pair and metric. Asterisks denote statistical significance of the improvement over the baseline, while daggers (

\dagger

) and double daggers (

\ddagger

) denote significance over 5 and 10 views, respectively, based on Holm-Bonferroni corrected p-values (

*:p<0.05,**:p<0.01,***:p<0.001

Table 3: Top-6 leaderboard rankings for the two subtasks across all language–domain pairs. Our submission is highlighted in bold .

Appendix figures & tables6 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 1: Prompt used for SCSG. The prompt comprises descriptions of the considered sentiment elements (4 for DimASTE, 5 for DimASQP), explanations regarding the range of valence and arousal, the desired output format, and the example text for which ABSA is to be performed.

Set	Domain	eng	jpn	rus	tat	ukr	zho
Train	Restaurant	2,284 / 3,659	—	1,240 / 2,487	1,240 / 2,487	1,240 / 2,487	6,050 / 8,523
	Laptop	4,076 / 5,773	—	—	—	—	3,490 / 6,502
	Hotel	—	1,600 / 2,846	—	—	—	—
Dev	Restaurant	200 / 408	—	48 / 102	48 / 102	48 / 102	300 / 761
	Laptop	200 / 317	—	—	—	—	300 / 551
	Hotel	—	200 / 364	—	—	—	—

Appendix

Table 4: Dataset statistics for DimASTE and DimASQP, reported as sentences / tuples per split, language, and domain. A dash (—) indicates that no data is available for the respective language–domain combination.

Input: “Decor is nice though service can be spotty.”
Predictions
Run	(Aspect Term, Sentiment Polarity) - Pair	Valence	Arousal	(Aspect Term, Sentiment Polarity) - Pair	Valence	Arousal
1	(Decor, nice)	6.92	7.13	(service, spotty)	5.53	6.03
2	(Decor, nice)	6.80	7.03	(service, be spotty)	5.60	6.10
3	(Decor, is nice)	6.67	6.90	(service, spotty)	5.40	5.90
4	(Decor, nice)	7.00	7.50	(service, spotty)	5.70	6.20

Appendix

Figure 2: Self-consistency majority voting for DimASTE over

k=5

runs. Aspect-sentiment pairs (ignoring valence-arousal values) appearing in

\geq\tau=\lceil k/2\rceil

runs are aggregated by averaging their valence and arousal values. The aggregation section shows the explicit calculation. Light blue rows highlight matching (Decor, nice) variants; light green rows highlight matching (service, spotty) variants. For DimASQP, in addition to the aspect term and sentiment polarity, the aspect category is considered as well.

		Subtask 2: DimASTE				Subtask 3: DimASQP
Language	Domain	BL	5 Views	10 Views	15 Views	BL	5 Views	10 Views	15 Views
English	Restaurant	77.93	78.35	78.15	78.45	75.17	75.30	75.39	75.26
English	Laptop	65.51	66.01	64.71	65.56	35.57	34.62	36.77	35.36
Japanese	Hotel	52.63	54.89	53.91	54.28	35.93	39.87	39.18	38.58
Russian	Restaurant	54.28	59.21	57.66	59.28	49.54	52.65	51.40	52.56
Tatar	Restaurant	52.72	52.83	52.99	53.54	38.65	44.65	43.95	43.56

Appendix

Table 5: Evaluation on the development set for DimASTE and DimASQP: cF1 scores across languages and domains comparing vanilla prompting (BL) and self-consistency (SC) using either 5, 10 or 15 prompt executions, with majority voting applied. Bold values indicate the best performance for each language–domain pair.

Language	Domain	#	Time (s)	Time/1K (s)	#	w/ SC		w/o SC		w/o SC + w/o Batch.
		Training			Evaluation
						Time (s)	Time/ex. (s)	Time (s)	Time/ex. (s)	Time (s)	Time/ex. (s)
English	Restaurant	2,284	3,860	1,690	1,000	320	0.321	37	0.037	1,151	1.151
English	Laptop	4,076	6,515	1,598	1,000	289	0.289	32	0.032	1,017	1.018
Japanese	Hotel	1,600	3,415	2,134	800	234	0.293	29	0.037	822	1.028
Russian	Restaurant	1,240	2,638	2,127	630	277	0.440	38	0.060	884	1.405

Appendix

Table 6: Training and evaluation duration across languages and domains for both DimASTE and DimASQP. We report duration with Self-Consistency (w/ SC, 15 views), without (w/o SC), and without vLLM’s batch processing (w/o Batch.).

Table 7: Evaluation on different LLMs (cF1). We compared G (Gemma-3-27B), M (Mistral-Small-24B), and Q (Qwen3-32B). Green cells ( x ) indicate performance surpassing the Rank 1 team on the official competition leaderboard for the respective language and domain combination. Overall, the results demonstrate that increasing the number of self-consistency views leads to consistent performance gains, with several configurations significantly outperforming the competition’s top-ranked entries.

Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) identifies what people express opinions about and the sentiment or emotion attached to each aspect.

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

Stavros Gazetas, Giorgos Filandrianos, Maria Lymperaiou, Paraskevi Tzouveli+2

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

Aspect-Based Sentiment Analysis

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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.

Abstract

Aspect-Based Sentiment Analysis

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