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This paper describes our system for SemEval-2026 Task 3, which focuses on predicting continuous valence and arousal scores. The task poses significant challenges due to variations in data scale and pragmatic ambiguities across languages. To address these disparities, we propose an Adaptive Dual-Track Framework that dynamically selects modeling strategies based on task characteristics. For semantically stable tasks, we apply a robust single baseline optimized with layer-wise learning rate decay (LLRD) to ensure stability. For high-ambiguity scenarios such as the Environmental Protection domain, we adopt a heterogeneous ensemble strategy to mitigate prediction variance. Experimental results demonstrate that our system consistently outperforms the initial standard baseline across all subtasks. Furthermore, our lightweight approach exhibits remarkable parameter efficiency, achieving highly competitive performance against newly introduced large language model (LLM) baselines. Additionally, ablation studies reveal that under regression settings, conventional regularization techniques, cross-lingual data transfer, and homogeneous ensemble learning can lead to negative transfer, confirming the necessity of strategically diverging approaches tailored to linguistic characteristics.

ACL 2026

SCU_Mesclab at SemEval-2026 Task 3: An Adaptive Dual-Track Framework for Dimensional Aspect-Based Sentiment Analysis

ensemble learning

affective computing

large language models

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
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workshop paper

This paper describes our system for the SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA). We participate in Track A (DimABSA) and Track B (DimStance), both of which involve Subtask 1 – predicting continuous valence–arousal (VA) scores for given text–aspect pairs in English and Chinese.
Our system combines pre-trained multilingual transformers with aspect-marker input encoding and dual regression heads for VA prediction, trained with a 5-fold cross-validation ensemble. We select XLM-RoBERTa-large as the backbone for Track A and mDeBERTa-v3-base for Track B based on systematic model comparison on the development sets. On the official test sets, our system substantially outperforms the organizer-provided baselines across all language domain settings. On the unofficial postevaluation leaderboard, the system achieves strong results on Chinese subsets, ranking 1st on zho-env (Track B) and 2nd on zho-fin (Track A).

YangS_team at SemEval-2026 Task 3: Transformer-Based Aspect-Aware Regression for Dimensional Sentiment and Stance Analysis

We present our system for SemEval-2026 Task 9: Multilingual Polarization Detection, a binary classification task spanning 22 languages. Our approach fine-tunes separate Gemma 3 models (12B and 27B parameters) per language using Low-Rank Adaptation (LoRA), augmented with synthetic data generated by a large language model (LLM). We employ three synthetic data strategies (direct generation, paraphrasing, and contrastive pair creation) using GPT-4o-mini, with a multi-stage quality filtering pipeline including embedding-based deduplication. We find that per-language threshold tuning on the development set yields 2 to 4% F1 improvements without retraining. We also use weighted ensembles of 12B and 27B model predictions with per-language strategy selection. Our final system achieves a mean macro-F1 of 0.811 across all 22 languages, ranking 2nd overall out of 60 participating teams, with 1st place finishes in 2 languages and top-3 in 8 languages. We also find that alternative architectures (XLM-RoBERTa, Qwen3) that showed strong development set performance suffered 30 to 50% F1drops on the test set, highlighting the importance of generalization.

PSK at SemEval-2026 Task 9: Multilingual Polarization Detection Using Ensemble Gemma Models with Synthetic Data Augmentation

This paper describes Team SoloSemantics' submissions to SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning. We began with lightweight neuro-symbolic knowledge-graph baselines, but a triplet-tuned MPNet bi-encoder produced stronger semantic separation in our experiments. We adopted a shared dense encoder family across both tracks and kept the KG and fusion variants as diagnostic baselines. Team SoloSemantics ranked 22nd on Track A and 9th on Track B. Our reproducibility audit further shows that the KG branch was often too sparse on short summaries to represent abstract narrative relations reliably under the current extraction pipeline.

SoloSemantics at SemEval-2026 Task 4: Triplet-Tuned MPNet for Story Similarity

SemEval-2026 Task 3 (DimABSA) includes Dimensional Aspect Sentiment Quadruplet Extraction (DimASQP), which requires extracting structured tuples—aspect term, aspect category, and opinion term—together with continuous valence–arousal (VA) values from reviews (Yu et al., 2026a). In this work, we participate in Track A, Subtask 3. We describe NYCU Speech Lab's submission for the Chinese Restaurant and Laptop domains. Our system is a post-processing ensemble over heterogeneous architectures: LoRA/QLoRA fine-tuned decoder-only LLMs, a fine-tuned encoder-only model, and (optionally) prompted API-based LLMs. To improve robustness under the continuous F1 (cF1) metric, we use validation-calibrated weighted voting for tuple selection and weighted VA fusion for numerical aggregation, with strict output validation to enforce task constraints. Experiments on a held-out validation split show consistent gains over single models and clarify the precision–recall trade-offs induced by the voting threshold. On the organizers' released (tentative) test leaderboard snapshot, our submission ranks first in both domains.

NYCU Speech Lab at SemEval-2026 Task 3: Heterogeneous Model Ensemble with Adaptive Weighted Voting for Dimensional Aspect Sentiment Quadruplet Extraction

Detecting and interpreting polarized online content is increasingly crucial as online platforms become central to public information exchange. We present an efficient adaptation of large language models for multi-label polarization classification in SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization. Our single-forward-pass inference method outperforms baseline multi-step decoding approaches for multi-label classification by reducing error propagation while improving inference efficiency. Beyond performance and efficiency analysis, we investigate the cross-lingual transferability of the system, observing statistically significant generalization within language families, a result that offers a practical path for low-resource language adaptation. Our system ranked 1st in 8 languages for Subtask 1 and 6 languages for Subtask 2, and placed in the top 5 for 16 out of 22 languages across both subtasks.
Overall, we provide a simple, effective, and efficient solution for multilingual polarization classification.

UTokyo Tsuruoka Lab at SemEval-2026 Task 9: Efficient Single Forward Pass Inference for Multi-Label Polarization Classification

We describe our system for SemEval-2026 - Task 8 (MTRAGEval), participating in Task A (Retrieval) across four English-language domains. Our approach employs a three-stage pipeline: (1) query rewriting via a LoRA-finetuned Qwen 2.5 7B model that transforms context-dependent follow-up questions into standalone queries, (2) hybrid BM25 and dense retrieval combined through Reciprocal Rank Fusion, and (3) cross-encoder reranking with BGE-reranker-v2-m3. On the official test set, the system achieves nDCG@5 of 0.531, ranking 8th out of 38 participating systems and 10.7% above the organizer baseline. Development comparisons reveal that domain-specific temperature tuning for query generation, where technical domains benefit from deterministic decoding and general domains from controlled randomness, provides consistent gains, while more complex strategies such as domain-aware prompting and multi-query expansion degrade performance.

Caraman at SemEval-2026 Task 8: Three-Stage Multi-Turn Retrieval with Query Rewriting, Hybrid Search, and Cross-Encoder Reranking

This paper presents our system for SemEval-2026 Task~11 Subtask~1 on content-independent syllogistic reasoning. The task evaluates whether language models can determine the formal validity of logical arguments independently of their semantic plausibility. To reduce content-driven biases, we propose a data augmentation strategy that progressively abstracts lexical semantics by replacing content words with symbolic placeholders and pseudo-words while preserving logical structure. Experiments based on fine-tuning microsoft/deberta-large-mnli show that abstraction-based augmentation reduces Content Effect and improves accuracy, leading to competitive performance on the official leaderboard. However, we observe substantial sensitivity to random initialization, suggesting that evaluation outcomes are partly influenced by stochastic factors. To better understand these effects, we conduct a layer-wise probing analysis using a Minimum Description Length framework, showing that the proposed approach decreases the accessibility of plausibility information in later transformer layers, indicating a shift toward more structure-oriented reasoning.

Team ewelinaksiez at SemEval-2026 Task 11: Reducing Content Bias in Syllogistic Reasoning via Semantic Abstraction

This paper describes the system developed by the LATE-iimas team for Task 10 of SemEval-2026: Psycomark, specifically for Subtask 2, which involves conspiracy detection. Our approach was based on fine-tuning the popular pre-trained language model DeBERTa-v3-Large. To address the challenges inherent in the provided dataset, such as class imbalance and the linguistic ambiguity of the "Can't tell" label, we implemented a 5-Fold Stratified Cross-Validation technique combined with a Weighted Cross-Entropy Loss function. The final system, which operates using an ensemble of the resulting models, achieved a Weighted F1-Score of 0.75, placing it in the top 10 of the ranking.

LATE-iimas at SemEval-2026 Task 10: Conspiracy Detection via DeBERTa-v3 Ensemble and Weighted Loss Optimization

We describe our SemEval-2026 Task 8 Subtask A system, which focuses on evaluating and improving the retrieval aspect of multi-turn Retrieval-Augmented Generation (RAG) conversations. We implement a training-free fusion approach that combines three distinct query representations to retrieve documents independently. The results from these three views are pooled and reranked using a MonoT5 cross-encoder. Our findings demonstrate that this fusion approach consistently outperforms single-strategy baselines, revealing that optimal retrieval strategies vary significantly at the query level, and establishing multi-query fusion as a baseline for multi-turn RAG systems.

GUIR at SemEval-2026 Task 8: Training-Free Multi-Query Fusion for Robust Conversational Retrieval

The generation of source code via Artificial Intelligence has become a prevalent practice in both academia and industry, posing significant challenges to academic integrity and authorship attribution. In this work, we address SemEval-2026 Task 13: Detecting Machine-Generated Code by evaluating the effectiveness of four distinct methodologies: Graph Neural Networks (GNNs), Pre-trained Language Models (PLMs), Large Language Models (LLMs), and Stylometric Feature Engineering using XGBoost. Our approach focuses on three specific scenarios: Subtask A (Binary Detection), Subtask B (Multi-Class Authorship), and Subtask C (Hybrid Code Detection). While our models achieved high performance during the validation phase, the transition to the final test set revealed substantial challenges in generalization, likely due to the increased diversity of programming languages and generators in the unseen data. This work serves as a foundational first step, identifying critical gaps in model robustness and highlighting the need for more sophisticated methodologies to bridge the performance gap in complex, real-world environments.

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