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This paper describes the system developed by team for SemEval-2026 Task 2, Subtask 1: Longitudinal Affect Assessment. Our goal is to predict Valence and Arousal from ecological essays and feeling words over time. We propose an efficient hybrid framework that uses quantized 7B-scale language models as deterministic meta-feature extractors and combines them with an ensemble of DeBERTa, RoBERTa, and DistilBERT encoders. The system is designed to run on a single consumer-grade RTX 5060 Ti (16GB) GPU while remaining competitive. To bridge discrete supervision and continuous evaluation, we train the model as an ordinal classification problem and decode class probabilities into continuous scores through expected-value decoding. Our best system achieved an overall V&amp;A average of 0.587, with per-dimension composite correlations of 0.647 for Valence and 0.527 for Arousal, ranking 3rd out of 31 teams. The results show that lightweight SLM-derived priors and multi-encoder fusion provide a strong performance--efficiency trade-off, especially for Arousal, where contextual anchoring is crucial.

ACL 2026

cclin at SemEval-2026 Task 2 : SLM-Enhanced Lightweight Multi-BERT Ensemble for Longitudinal Affect Assessment

multi-bert ensemble

lightweight hybrid models

longitudinal affect prediction

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

This paper describes the system developed by team for SemEval-2026 Task 2, Subtask 1: Longitudinal Affect Assessment. Our goal is to predict Valence and Arousal from ecological essays and feeling words over time. We propose an efficient hybrid framework that uses quantized 7B-scale language models as deterministic meta-feature extractors and combines them with an ensemble of DeBERTa, RoBERTa, and DistilBERT encoders. The system is designed to run on a single consumer-grade RTX 5060 Ti (16GB) GPU while remaining competitive. To bridge discrete supervision and continuous evaluation, we train the model as an ordinal classification problem and decode class probabilities into continuous scores through expected-value decoding. Our best system achieved an overall V&A average of 0.587, with per-dimension composite correlations of 0.647 for Valence and 0.527 for Arousal, ranking 3rd out of 31 teams. The results show that lightweight SLM-derived priors and multi-encoder fusion provide a strong performance--efficiency trade-off, especially for Arousal, where contextual anchoring is crucial.

workshop paper

Multi-turn retrieval-augmented generation (RAG) is challenging due to evolving user intent, conversational noise, and strict context limits. We propose a training-free hybrid retrieval pipeline for SemEval-2026 Task 8 that combines dense and sparse retrieval with controlled query rewriting and cross-encoder reranking. Our system achieves 0.5453 nDCG@5 on the official test set of Task A, ranking 3rd out of 38 teams and outperforming the strongest baseline (0.4795). For Task C, we reuse the Task A retrieved documents in a lightweight generation pipeline based on the official prompt, achieving 0.5312 (harmonic mean of quality and faithfulness) and ranking 15th out of 29 teams. All retrieval components are open-source, while rewriting and generation use LLM APIs. Code and scripts are available on GitHub (https://github.com/mengsifei/Multi_turn_RAG).

Sifei at SemEval-2026 Task 8: Hybrid Retrieval and Query Rewriting for Multi-Turn RAG

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.

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

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.

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