United States

SemEval-2026 Task 11 evaluates the ability of Large Language Models (LLMs) to perform content-independent reasoning through a novel multilingual syllogistic dataset designed to measure the &quot;content effect&quot; — the tendency to conflate semantic plausibility with logical validity. The competition featured four subtasks, covering English and multilingual settings with both standard and noisy premise sets. Evaluations of zero-shot baselines reveal that the content effect is pervasive in open models, whereas newer versions demonstrate a significant shift in performance. Across the subtasks, findings indicate that introducing distracting premises can challenge the models&#39; ability to filter misleading information, while multilingual settings amplify their susceptibility to content biases compared to English. Participants proposed diverse approaches, including neuro-symbolic decomposition, fine-tuning and distillation methods, data augmentation, and activation steering. While explicit symbolic verification remains the most reliable strategy, activation-level interventions and fine-tuning methods offer promising pathways for internalising formal logic within neural architectures. Ultimately, the task reinforces the efficacy of neuro-symbolic approaches and emerging architectural trends for logical reliability, while also highlighting that multilingual setups and longer contexts still pose significant challenges to be investigated in future research.

ACL 2026

SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models

content effect

syllogistic reasoning

synthetic data augmentation

llms

multi-objective optimization

multilingual nlp

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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

SemEval-2026 Task 11 evaluates the ability of Large Language Models (LLMs) to perform content-independent reasoning through a novel multilingual syllogistic dataset designed to measure the "content effect" — the tendency to conflate semantic plausibility with logical validity. The competition featured four subtasks, covering English and multilingual settings with both standard and noisy premise sets. Evaluations of zero-shot baselines reveal that the content effect is pervasive in open models, whereas newer versions demonstrate a significant shift in performance. Across the subtasks, findings indicate that introducing distracting premises can challenge the models' ability to filter misleading information, while multilingual settings amplify their susceptibility to content biases compared to English. Participants proposed diverse approaches, including neuro-symbolic decomposition, fine-tuning and distillation methods, data augmentation, and activation steering. While explicit symbolic verification remains the most reliable strategy, activation-level interventions and fine-tuning methods offer promising pathways for internalising formal logic within neural architectures. Ultimately, the task reinforces the efficacy of neuro-symbolic approaches and emerging architectural trends for logical reliability, while also highlighting that multilingual setups and longer contexts still pose significant challenges to be investigated in future research.

workshop paper

We present our shared task on predicting variation in emotional valence and arousal over time from ecological essays. The shared task uses a longitudinal dataset collected over 7 data collection phases of 14-day each spanning from 2021 to 2024, consisting of real-time essays and feeling words (e.g., happy, calm, sad, etc.) written in English by U.S. service-industry workers about ``how they are feeling''. Each text is associated with self‑reported valence (V) (0 - 4, highly negative to highly positive affect) and arousal (A) (0 - 2, low to high energy) scores. The shared task consists of three parts, Subtask (1): Longitudinal Affect Assessment, Subtask (2): Forecasting Variation in Affect as a (2a): \textit{state change}, and (2b): \textit{disposition change}.

The task attracted over 200 member registrations on Codabench, receiving official system submissions from 31 teams (total 104 team members), of which 28 teams (with 90 team members) submitted system description papers making it to our leaderboard. We discuss baseline results along with findings from 28 systems, highlighting the best-performing systems, a deeper analysis of performance on essays versus feeling words, and assessments for authors seen versus unseen during training. The datasets for this task are publicly available.

SemEval-2026 Task 2: Predicting Variation in Emotional Valence and Arousal over Time from Ecological Essays

We present the SemEval-2026 shared task on Dimensional Aspect-Based Sentiment Analysis (DimABSA), which improves traditional ABSA by modeling sentiment along valence–arousal (VA) dimensions rather than using categorical polarity labels. To extend ABSA beyond consumer reviews to public-issue discourse (e.g., political, energy, and climate issues), we introduce an additional task, Dimensional Stance Analysis (DimStance), which treats stance targets as aspects and reformulates stance detection as regression in the VA space. The task consists of two tracks: Track A (DimABSA) and Track B (DimStance). Track A includes three subtasks: (1) dimensional aspect sentiment regression, (2) dimensional aspect sentiment triplet extraction, and (3) dimensional aspect sentiment quadruplet extraction, while Track B includes only the regression subtask for stance targets. We also introduce a continuous F1 (cF1) metric to jointly evaluate structured extraction and VA regression.

The task attracted more than 400 participants, resulting in 112 final submissions and 42 system description papers. We report baseline results, discuss top-performing systems, and analyze key design choices to provide insights into dimensional sentiment analysis at the aspect and stance-target levels. All resources are available on our GitHub repository.

SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)

We present SemEval-2026 Task 1: MWAHAHA (Models Write Automatic Humor And Humans Annotate), the first shared task on general-purpose humor generation. Systems must produce short jokes in English, Spanish, and Chinese under lexical or topical constraints (Subtask A) and generate humorous captions for GIFs (Subtask B). To discourage memorization and ensure fairness, all jokes must meet specific criteria, such as using infrequent word pairs or relating to recent news headlines. Evaluation is conducted through pairwise human preference judgments in a Chatbot Arena-style setting, yielding Elo-based rankings. The task attracted 309 registered users, with 37 teams submitting systems to the evaluation phase. Participating systems employ a wide range of NLP techniques, including generate-then-rank pipelines, reinforcement learning, parameter-efficient fine-tuning, retrieval-augmented generation, humor-theory-grounded prompting, and persona-based strategies. Our Gemini 2.5 Flash baseline, using simple prompts, tied for first place in all subtasks, and the majority of elaborate multi-stage pipelines only marginally surpassed it with overlapping confidence intervals. More work is necessary to outperform the simple usage of state-of-the-art large language models. We release all evaluation data, prompts, and leaderboard results to support future research in computational humor generation.

SemEval-2026 Task 1: MWAHAHA, Models Write Automatic Humor And Humans Annotate

We tackle classifying evasive political answers
within the context of SemEval-2026 Task 6 and
compare three modeling strategies: a flat base-
line, a hierarchical cascade, and a multitask
learning approach. Our experiments demon-
strate that a hierarchical RoBERTa-base model
achieves the best performance, particularly by
leveraging the distinctiveness of the class Clear
Non-Reply. Conversely, we find that stan-
dard multitask learning frequently produces
structurally invalid label combinations in a sig-
nificant fraction of predictions. Our demon-
strations show that applying a constrained in-
ference mask eliminates these errors entirely
while improving F1 performance, whereas a
fully joint training approach underperforms due
to data sparsity. Finally, we employ dataset
cartography to compare training dynamics be-
tween the hierarchical and multitask approach.

FER at SemEval-2026 Task 6: Analysis of Different Approaches to Unmasking Political Question Evasions

This paper addresses SemEval-2026 Task 11, which focused on mitigating content effects in syllogistic reasoning. Logical validity is often conflated with semantic plausibility in large language models.
Prior methods rely on standard fine-tuning or prompting, without explicit bias control.
A rule- and template-based symbolic data augmentation framework is proposed for fine-tuning the \texttt{Qwen2-1.5B-Instruct} model and instruction-tuning the \texttt{XLM-RoBERTa-large} model. 
Logic-preserving synthetic data are generated through lexical rules. 
The system is ranked 1st in Task 1 with a perfect overall score of 100, and 6th in Task 3 with a score of 56.97. Code is publicly available at: \url{https://github.com/YNU-HPCC/semeval-2026-task11}.

YNU-HPCC at SemEval-2026 Task 11: Mitigating Content Effects in Syllogistic Reasoning with Qwen2-1.5B-Instruct and XLM-RoBERTa-Large for English and Multilingual TasksMultilingual Tasks

This paper presents the English-language submissions of The Counterfactuals team for the three subtasks of Task 9 at SemEval 2026. The task aims to detect multicultural online polarization, how it is expressed, and in what contexts. The task provides a high-quality annotation dataset of posts that follows a three-level schema: polarized or not (subtask 1), polarization type classification (subtask 2), and manifestation identification (subtask 3). I construct a pointwise mutual information-based lexicon that identifies highly-correlated words with the polarized class as labeled in subtask 1. Using this lexicon, I implement a large language model data augmentation technique. I then use the preprocessed datasets to finetune a BERT model (BERTweet) for each subtask. My highest performing models placed 48th out of 60, 35th out of 36, and 17th out of 24 on subtasks 1, 2, and 3 respectively. All code is available on GitHub.

The Counterfactuals at SemEval-2026 Task 9: Can Counterfactually-Inspired Preprocessing help Detect Polarization?

For the Chinese track of SemEval-2026 Task 9 (Detecting Online Polarization), we address two key challenges: polarized content frequently uses implicit language (e.g., homophones and coded terms) to evade moderation, and class distributions exhibit severe long-tail imbalance. We propose a metric learning approach that frames polarization detection as semantic similarity matching, which captures implicit language patterns better than linear decision boundaries. We fine-tune an ERNIE-3.0 encoder with SoftTriple loss and apply kNN retrieval for binary detection (Subtask 1). For multi-label categorization (Subtasks 2 and 3), we transfer learned representations from the detection model and fine-tune with Asymmetric Loss. A priority-based stratified cross-validation strategy ensures minority classes appear across all training folds despite extreme label skew. Evaluated on the official 1,927-sample test set using an end-to-end pipeline, our system achieved Macro-F1 scores of 0.9190 (Rank 6) on Polarization Detection, 0.8244 (Rank 5) on Type Classification, and 0.6670 (Rank 4) on Manifestation Identification.

AlphaLyrae at SemEval-2026 Task 9: Metric Learning and Asymmetric Loss for Chinese Polarization Analysis

This paper presents the Duluth approach to
SemEval-2026 Task 6 on CLARITY: Unmask-
ing Political Question Evasions. We address
Task 1 (clarity-level classification) and Task 2
(evasion-level classification), both of which in-
volve classifying question–answer pairs from
U.S. presidential interviews using a two-level
taxonomy of response clarity. Our system is
based on DeBERTa-V3-base, extended with
focal loss, layer-wise learning rate decay, and
boolean discourse features. To address class
imbalance in the training data, we augment
minority classes using synthetic examples gen-
erated by Gemini 3 and Claude Sonnet 4.5. Our
best configuration achieved a Macro F1 of 0.76
on the Task 1 evaluation set, placing 8th out
of 40 teams. The top-ranked system (TeleAI)
achieved 0.89, while the mean score across par-
ticipants was 0.70. Error analysis reveals that
the dominant source of misclassification is con-
fusion between Ambivalent and Clear Reply
responses, a pattern that mirrors disagreements
among human annotators. Our findings demon-
strate that LLM-based data augmentation can
meaningfully improve minority-class recall on
nuanced political discourse tasks.

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

We present a lightweight, feature-based regression system for predicting \textbf{valence} (pleasantness) and \textbf{arousal} (activation) from longitudinal language data. The language data ranges from longer free-form ecological essays to short affect-word, organized by user and time, reflecting natural variation in affective expression and experience. Our approach combines three complementary signals: (i) sentence-level semantic embeddings, (ii) psycholinguistic category features capturing affect- and function-related word usage, (iii) similarity measures between the language data with archetypal sentences, and (iv) trainable user-embeddings to account for between-user differences. The resulting feature vector is passed to a multi-layer perceptron trained to jointly predict valence and arousal. Our design provides a strong and interpretable baseline by making it possible to isolate the contribution of semantic, psycholinguistic, similarity, and user-specific signals. We further analyze our model's predictions to identify which feature groups are most informative and where errors are concentrated across users and input types.

McMaster NLP at SemEval-2026 Task 2: A Lightweight Multi-Feature System for Predicting Emotional Valence and Arousal over Time

We describe our system for SemEval-2026 Task 6 (CLARITY: Unmasking Political Question Evasions), which classifies English political interview responses by coarse-grained clarity (3-way) and fine-grained evasion strategy (9-way). Since responses frequently exceed the 512-token limit of standard Transformer encoders, we apply an overlapping sliding-window chunking strategy with element-wise Max-Pooling aggregation over chunk representations. A shared RoBERTa-large encoder supplies two task-specific heads trained jointly via a multi-task objective, with inference-time ensembling over 7-fold stratified cross-validation. Our system achieves a Macro-F1 of 0.80 on Subtask 1 and 0.51 on Subtask 2, ranking 11th in both subtasks.

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