United States

We describe our research to SemEval-2026 Task 4 on Narrative Story Similarity and Narrative Representation Learning (NSNRL). The shared task defines narrative similarity through comparative judgments over triples consisting of an anchor story and two candidates, where systems determine which candidate is narratively closer (Track A), and must output story embeddings whose cosine distances reproduce the same ordering under withheld evaluation triples (Track B). We implement a unified representation-learning approach based on a Sentence-BERT bi-encoder trained with triple-derived metric learning objectives, combining in-batch contrastive learning with explicit triplet and margin-ranking constraints. Track A is solved by direct cosine comparison between the anchor embedding and each candidate embedding, while Track B outputs normalized story vectors from the same encoder without any additional test-time modelling. During evaluation, we achieve 65.00% accuracy on Track A and 65.50% on Track B. These results suggest that a single, well-aligned bi-encoder can perform competitively across both tracks while remaining computationally efficient.

ACL 2026

MarSan at SemEval-2026 Task 4: Narrative Similarity via Sentence-BERT Metric Learning with Triple-Derived Losses

narrative similarity

llms

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

Team PICT's submission for SemEval-2026 Task 3 (DimASR) tackles continuous valence and arousal prediction by heavily focusing on variance reduction and avoiding cross-domain negative transfer. We built strictly domain-isolated pipelines for the Laptop and Restaurant datasets using a RoBERTa-Large backbone. Our architecture extracts a rich feature hierarchy using weighted layer pooling, isolates local context with a [CLS]-driven aspect-aware attention module, and maps to the continuous space using a deep residual regression head. Regularized via R-Drop and SWA, our system achieved 3rd place in the Restaurant domain (RMSE: 1.195) and 9th in the Laptop domain (RMSE: 1.326).

PICT at SemEval-2026 Task 3: A Transformer-Based System for Dimensional Aspect-Aware Sentiment Regression with Weighted Layer Pooling

This paper presents our system developed for SemEval-2026 Task 2. The task requires modeling both current affect and short-term affective change in chronologically ordered user-generated texts. We explore three complementary approaches: (1) LLM prompting under user-aware and user-agnostic settings, (2) a pairwise Maximum Entropy (MaxEnt) model with Ising-style interactions for structured transition modeling, and (3) a lightweight neural regression model incorporating recent affective trajectories and trainable user embeddings. Our findings indicate that LLMs effectively capture static affective signals from text, whereas short-term affective variation in this dataset is more strongly explained by recent numeric state trajectories than by textual semantics.

UKP_Psycontrol at SemEval-2026 Task 2: Modeling Valence and Arousal Dynamics from Text

We present Paradise, our system for SemEval-2026 Task 12: Abductive Event Reasoning, which identifies plausible direct causes of real-world English-language events using retrieved contextual documents. Our approach employs Qwen2.5-7B-Instruct, a 7-billion-parameter instruction-tuned language model combined with carefully engineered chain-of-thought prompting, requiring no task-specific fine-tuning or training-data supervision (prompt components were selected using the development set). The system achieves a score of 0.79 on the official 612-instance test set by integrating explicit causal-inference rules, 4,000-character document context windows, and greedy decoding. Analysis reveals that conservative prediction patterns, 87.1% single-label and 36.9% Option D, effectively exploit the asymmetric scoring metric. Ablation studies confirm that document context contributes +6.4 points, chain-of-thought reasoning +5.3 points, and explicit causal rules +3.1 points to development performance. Our code is publicly available at https://github.com/DhruvGoyal404/semeval2026-task12.

Paradise at SemEval-2026 Task 12: Leveraging Instruction-Tuned Large Language Models with Chain-of-Thought Prompting for Abductive Event Reasoning

This paper introduces a simple approach for predicting how plausible a word sense is in short narratives where meaning is ambiguous. We use 13 hand-crafted features, including text statistics, word-level similarity computed using basic set-based comparisons, and measures of annotator disagreement. Five diverse and largely independent traditional machine learning models are combined using a weighted ensemble with minimal tuning. Despite theoretical grounding in classical disambiguation methods, our system achieves essentially random performance, with Spearman correlation (ρ) of −0.038 and accuracy within standard deviation of 0.542 on the official test set. This result demonstrates that surface-level lexical features, while interpretable, are insufficient for graded sense plausibility prediction without deep semantic representations. By selecting features inspired by classical word sense disambiguation techniques and incorporating signals derived from human disagreement, our model produces plausibility predictions that are largely interpretable. This negative result provides important baselines and insights for future work on graded word sense disambiguation.

Paradise at SemEval-2026 Task 5: On the Limitations of Surface-Level Features for Graded Word Sense Plausibility Prediction

In this paper, we present our submission to the SemEval-2026 Psycholinguistic Conspiracy Shared Task (Task 10), which consists of two tasks: conspiracy marker extraction and conspiracy detection. For conspiracy marker extraction, we formulate the problem as a token classification task and fine-tune pretrained language models, achieving performance above the official baseline and ranking 6th on the final leaderboard. For conspiracy detection, we apply data preprocessing, regularization, and ensemble inference strategies,resulting in improvements over the baseline and a 10th-place ranking. Overall, our results demonstrate the effectiveness of pretrained language models for both tasks.

YNU-HPCC at SemEval-2026 Task 10: Pretrained DistilBERT Models for Conspiracy Marker Extraction and Detection

This paper describes our systems for SemEval-2026 Task 11, Disentangling Content and Formal Reasoning in Language Models. We participated in all four subtasks, addressing the Content Effect-a phenomenon where models rely on real-world plausibility rather than logical validity. Existing methods, such as standard Chain-of-Thought (CoT) prompting or single-task Supervised Fine-Tuning (SFT), often struggle to completely decouple content from reasoning due to the inherent probabilistic biases in pre-trained models. To address these limitations, a hybrid neuro-symbolic framework based on the Qwen2.5-14B architecture is proposed, integrating multi-task instruction tuning with a robust neuro-symbolic pipeline. The principal innovation lies in the deployment of a Reflexion mechanism coupled with formal verification: natural language arguments are parsed into First-Order Logic (FOL) and subsequently verified by the Z3 Theorem Prover. Parsing anomalies are automatically rectified through an iterative self-correction module. The proposed system ranked 1st in Subtasks 1 & 2, 2nd in Subtask 4, and 9th in Subtask 3, validating its ability to decouple logical validity from content plausibility.

YNU-NLP at SemEval-2026 Task 11: A Neuro-Symbolic Approach with Reflexion Mechanism Disentangling Content and Formal Reasoning in Language Models

This paper presents our approach for the SemEval-2026 Task 8: MTRAGEval (SubtaskB: Answer Generation), which challenges sys-tems to generate faithful, extractive answers to multi-turn questions based strictly on provided gold-standard reference passages. The primary scientific challenge lies in maintaining high faithfulness and structural consistency while adapting to diverse answer styles across a conversation, as systems must generate responses that vary significantly in length and format without hallucinating. Conventional reference-based generation methods often rely on static prompting or greedy decoding, which fail to capture these dynamic stylistic requirements and lack robustness against generation noise. To address these limitations, we propose a Intent-Aware Parallel Generation and Reranking System powered by a large language model. Experimental results on the official test set demonstrate the effectiveness of our method, achieving competitive performance comparable to SoTA baselines. Ultimately,our approach secured the third place in the competition. The code of the paper is available at: https://github.com/viaviachris/SemEval-2026-Task8

YNU-HPCC at SemEval-2026 Task 8: Parallel Generation and Multi-Metric Reranking for Faithful Extractive RAG

We propose and validate a multi-view ensemble framework for 4-class AI-generated code detection (Human, AI, Hybrid, Adversarial) in realistic long-form repositories. Our system, Team YoungDSMLKZ, ranked 1st out of 50+ teams in SemEval-2026 Task 13 Subtask C with a macro F1 of 0.7855 (+5.2 over runner-up). The framework combines: (i) a Dynamic Multiple Instance Learning (MIL) pipeline over UniXcoder chunks for O(N)-scalable long-context detection, (ii) transformer-based meta-stacking (UniXcoder and ModernBERT), and (iii) an XGBoost classifier on 200+ handcrafted stylometric features. Evidence localization analysis shows that 62.4% of decisive AI-detection signals reside beyond the standard 512-token window, validating the MIL design.

YoungDSMLKZ at SemEval-2026 Task 13: MIL-UniXcoder with Meta-Stacking and Handcrafted Features for AI-Generated Code Detection

This paper describes our system for SemEval 2026 Task 10 (PsyCoMark), focusing on Subtask 2: binary conspiracy classification in Reddit submission statements. We present a heterogeneous ensemble approach that combines Transformer-based models (DeBERTa, RoBERTa) with State-Space Models (Mamba) to leverage architectural diversity for improved generalization. Our key contributions include: (1) Bidirectional Mamba (BiMamba), adapting state-space sequence models for bidirectional document classification; (2) (2) a safety-switched multi-task training setup that uses marker supervision only for gold-annotated samples, preventing noisy pseudo-labeled rows from affecting the span extraction objective; and (3) Confidence-Based Iterative Refinement, using committee voting for high-quality pseudo-label generation. Our best official submission achieved a weighted F1 score of 0.78 on the Subtask 2 test set, ranking 4th on the public CodaBench leaderboard. We provide detailed ablation studies demonstrating the complementary contributions of each architectural component to inform future research directions.

VARH-AI at SemEval-2026 Task 10: Exploiting Architectural Diversity with Transformer-SSM Ensembles and Confidence-Based Iterative Refinement for Conspiracy Detection

While Large Language Models (LLMs) excel in many general NLP tasks, their formal reasoning capabilities are often compromised by content effects, demonstrating a measurable bias towards real-world plausibility. In this paper, we present our system for SemEval-2026 Task 11, which evaluates the ability of models to disentangle formal logic from content across 12 languages with and without distractor premises. We address this challenge using mDeBERTa-v3 networks fine-tuned on a synthetic, rule-based dataset of syllogistic schemes to avoid the semantic noise of LLM-augmented data. To explicitly decouple plausibility from logical structure, our training pipeline employs a multi-objective loss function combining Adaptive Group Distributionally Robust Optimization (DRO), a scheduled differentiable bias penalty, and KL-Divergence consistency regularization. Our system achieved #1 ranks and perfect Ranking Scores (100.0) with 0.00% bias and 100.0% accuracy on Subtask 1 (English), Subtask 2 (Noisy English), and Subtask 3 (Multilingual). On the highly complex Subtask 4 (Noisy Multilingual), the system achieved the 6th rank with 89.06% Accuracy and F1-score, alongside a limited 2.89% Bias and a 37.78 Ranking Score. Our dataset generation engine and codebase are publicly available to facilitate future work on robust logical reasoning.

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