United States

This paper describes the PFW system for SemEval-2026 Task 6 (CLARITY), which addresses the classification of response clarity and evasion techniques in political interview question-answer pairs. Rather than relying on large language model prompting, we pursue a competitive non-LLM approach based on fine-tuning DeBERTa-xlarge and DeBERTa-v3-large with a multi-seed ensemble strategy: 5-fold cross-validation with 10 random seeds yields 50 models per architecture, combined through simple logit averaging. Our system achieves a macro F1 of 0.76 on Subtask 1 (clarity-level classification) and 0.50 on Subtask 2 (evasion-type classification). We also find that three post-hoc optimization techniques—learned ensemble weights, thresh old calibration, and hierarchical masking— each improve out-of-fold performance yet degrade evaluation scores by 0.02–0.10 F1. This pattern should be interpreted cautiously: the 237-sample evaluation set likely contributes substantial variance, and two of the three degradations fall within the ±0.06 95% CI expected from sampling noise. Still, the consistent directional pattern across all three prediction-level interventions provides suggestive evidence for an optimization paradox, highlighting the risk of overfitting to cross-validation predictions when evaluation data is limited. Our code is publicly available at https://github.com/ Taleef7/semeval-2026-task6.

ACL 2026

PFW at SemEval-2026 Task 6: Multi-Seed DeBERTa Ensembles for Political Response Clarity and Evasion Classification

response clarity

question evasion

deberta

political discourse

semeval

ensemble learning

classification

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.

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workshop paper

We describe PFW Task 8's system for SemEval 2026 Task 8 (MTRAGEval), a benchmark for multi-turn retrieval-augmented generation across four English-language corpora. Our submission combines BM25, SPLADE-v3, and Jina Embeddings v4 with weighted reciprocal rank fusion for retrieval, plus zero-shot GPT 4o/GPT-4o-mini prompting for generation. Officially, our system ranks 6th of 26 on Task B (H = 0.756), 14th of 29 on Task C (H = 0.533), and 20th of 38 on Task A (nDCG@5 = 0.433). For the camera-ready analysis, we re-run retrieval at the official nDCG@5 cutoff, strengthen the prompt ablation with per-domain statistics and exact tests, and analyze official outputs by answerability and domain. On a balanced 100-example development sample, explicit citation-format instructions—not chain of-thought alone—raise citation use from 4% to 93%, and a fixed-context Task C control improves from H = 0.463 with GPT-4o-mini to H = 0.523 with GPT-4o. Official analytics also show near-perfect UNANSWERABLE handling (H = 0.990) but weak behavior on UNDERSPECIFIED turns, where the system answers or refuses instead of clarifying. Our code is publicly available.

PFW Task 8 at SemEval-2026 Task 8: Lightweight Tri-Fusion Retrieval with Prompt-Engineered Faithful Generation for Multi-Turn RAG

Online polarization, the critical division between social, political, or identity groups, often leads to hate speech and social fragmentation. Detecting polarization, especially across diverse linguistic and cultural contexts, is a critical challenge. This paper presents our submission for SemEval-2026 Task 9, which focuses on detecting online polarization of multilingual, multicultural, and multievent (Naseem et al., 2025). The task is divided into three subtasks: (1) binary polarization detection, (2) multi-label classification of polarization type (e.g., political, racial, religious), and (3) multilabel identification of its manifestation (e.g., stereotype, vilification, dehumanization). For each subtask, we employ fine tune BERT-based transformer models. Model configurations are described in Section 4. The results are evaluated using the F1 macro score. We have achieved scores of 78.6, 55.8, 44.6 on the development
test set for subtasks 1, 2, and 3, respectively. Overall, the results demonstrate the effectiveness of BERT-based models for multilingual polarization detection.

MSqrd at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization

This paper describes our first-place submission to Task B (generation with reference passages) of the SemEval-2026 Task 8 MTRAGEval shared task on multi-turn retrieval-augmented generation. We propose a heterogeneous ensemble of seven LLMs organised into two groups with distinct prompting strategies, and use a GPT-4o-mini judge to select the best candidate response for each instance. Our system ranked first among 26 teams, achieving a conditioned harmonic mean score of 0.78 and substantially outperforming the strongest 
organiser baseline (0.64). Ablation experiments show that diversity across model families, scales, and prompting strategies is critical: the ensemble consistently outperforms any individual model. We also include Meno-Lite-0.1, a 7B domain-adapted model with a favourable cost--performance trade-off, and present an analysis of MTRAGEval that highlights annotation limitations and directions for benchmark improvement.

RaguTeam at SemEval-2026 Task 8: Meno and Friends in a Judge-Orchestrated LLM Ensemble for Faithful Multi-Turn Response Generation

Large language models suffer from content effects in reasoning tasks, particularly in multilingual contexts. We introduce a novel method that reduces these biases through explicit structural abstraction that transforms syllogisms into canonical logical representations and applies deterministic parsing to determine validity. Evaluated on the SemEval-2026 Task 11 multilingual benchmark, our approach achieves top-5 rankings across all subtasks while substantially reducing content effects and offering a competitive alternative to complex fine-tuning or activation-level interventions.

ITLC at SemEval-2026 Task 11: Normalization and Deterministic Parsing for Formal Reasoning in LLMs

This paper presents an uncertainty-aware adversarial learning framework developed for SemEval-2026 Task 3, a shared task focusing on Dimensional Aspect-Based Sentiment Analysis (ABSA). Our framework involves three key components: Uncertainty modeling, Heterogeneous Mixture-of-Experts (HMoE) architecture, and embedding-level adversarial training. Experimental results demonstrate that our framework effectively reduces the Root Mean Square Error (RMSE), thereby validating the synergistic advantages of uncertainty modeling and heterogeneous fusion strategies in fine-grained sentiment regression tasks.

Scmhl5 at SemEval-2026 Task 3: Uncertainty-Aware Adversarial Learning for Embedding Enhancement in Dimensional Aspect-Based Sentiment Analysis

The Dimensional ABSA (DimABSA) shared
task extends traditional aspect-based sentiment
analysis from categorical polarity to continuous
valence–arousal (VA) prediction. We present
our system for all three subtasks: Dimensional
Aspect Sentiment Regression (DimASR),
Dimensional Aspect Sentiment Triplet Extrac-
tion (DimASTE), and Dimensional Aspect
Sentiment Quad Prediction (DimASQP).
Due to the cascading nature of the different
subtasks, we built a modular interlocking
pipeline that uses classical Machine Learning
and NLP methods.
Experiments across domains show consistent
gains in regression accuracy and structured
extraction performance. Our results demon-
strate the effectiveness of distribution-aware
regression, retrieval-augmented generation, and
contrastive prototype learning for dimensional
sentiment analysis.

SokraTUM at SemEval-2026 Task 3: A hybrid cascade of Label Distribution Learning, RAG supported generative extraction and contrastive metric learning for dimensional sentiment analysis

This paper presents our winning system for SemEval-2026 Task 5 on rating the plausibility of word senses in ambiguous stories. Unlike traditional Word Sense Disambiguation, the task requires predicting continuous plausibility scores that reflect human variability rather than selecting a single correct sense. We propose a multi-target fine-tuning framework for decoder-only large language models that jointly optimizes regression for score prediction and text generation for interpretable explanations. To address inter-annotator variability, we adopt objective-level strategies to enhance robustness. Our system achieves first place, demonstrating the effectiveness of unified regressive–generative modeling for fine-grained plausibility estimation.

SRCB at SemEval-2026 Task 5 A Multi-Target Finetuning Framework for Large Language Models with Joint Regression and Text Generation

We propose a CodeBERT-based system for detecting AI-generated code under severe cross-language and cross-domain distribution shift. Our approach conducts a controlled comparison between a fully frozen backbone and a partially fine-tuned configuration that unfreezes only the final transformer layer with discriminative learning rates. While partial fine-tuning substantially improves in-domain performance, the frozen backbone demonstrates stronger robustness under out-of-distribution evaluation. Our results highlight a trade-off between task adaptation and cross-language generalization in machine-generated code detection.

Team_Omega at SemEval-2026 Task 13: Frozen vs. Trainable Representations for Out-of-Distribution AI-Generated Code Detection: A CodeBERT Fine-Tuning Study

The Narrative Story Similarity and Narrative Representation Learning (NSNRL) task measures the narrative similarity between two stories based on three core aspects: the abstract theme, the course of action, and the outcomes. Our system leverages LLMs both for extracting high-level aspects and to encode them with state-of-the-art generalist embedding models. We then apply a series of embedding post-processing steps and learn to fit the embedding space with a Mahalanobis-like diagonal metric. We show that some of these techniques should not be applied universally, as they do not necessarily increase performance or overfit, depending on the base encoder. Our system outperforms the baseline only in Track B, ranking twelfth out of twenty-seven on the final leaderboard, while performing lower than the baseline accuracy in Track A.

Team UBSE at SemEval-2026 Task 4: Adapting Generalist Embeddings for Narrative Representations

This paper presents a neuro-symbolic ensemble for determining narrative similarity by moving beyond surface-level text matching toward structural and causal alignment. The architecture fuses three primary signals: action-focused neural embeddings that isolate event trajectories , a symbolic Structural Survival Ratio (SSR) that measures the preservation of discrete event tuples via dependency parsing , and high-level structural comparisons conducted by the gpt-5-mini model. Evaluated on the SemEval-2026 Task 4 test set, the integrated ensemble achieved an accuracy of 68.25%.

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PFW Task 8 at SemEval-2026 Task 8: Lightweight Tri-Fusion Retrieval with Prompt-Engineered Faithful Generation for Multi-Turn RAG

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PFW Task 8 at SemEval-2026 Task 8: Lightweight Tri-Fusion Retrieval with Prompt-Engineered Faithful Generation for Multi-Turn RAG

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Duluth at SemEval-2026 Task 6: DeBERTa with LLM-Augmented Data for Unmasking Political Question Evasions

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