This paper presents a framework for perceived emotion intensity prediction, focusing on SemEval-2025 Task 11 Track B. The task involves predicting the intensity of five perceived emotions—anger, fear, joy, sadness, and surprise—on an ordinal scale from 0 (no emotion) to 3 (high emotion). Our approach builds upon our method introduced in the WASSA workshop and enhances it by integrating ModernBERT in place of the traditional BERT model within a boosting-based ensemble framework. To address the difficulty in capturing fine-grained emotional distinctions, we incorporate class-preserving mixup data augmentation, a custom Pearson CombinLoss function, and fine-tuned transformer models, including ModernBERT, RoBERTa, and DeBERTa. Compared to individual fine-tuned transformer models (BERT, RoBERTa, DeBERTa and ModernBERT) without augmentation or ensemble learning, our approach demonstrates significant improvements. The proposed system achieves an average Pearson correlation coefficient of 0.768 on the test set, outperforming the best individual baseline model. In particular, the model performs best for sadness (r = 0.808) and surprise (r = 0.770), highlighting its ability to capture subtle intensity variations in the text. Despite these improvements, challenges such as data imbalance, performance on low-resource emotions (e.g., anger and fear), and the need for refined data augmentation techniques remain open for future research.

In this paper, we propose a sieve filtering-based approach that can retrieve facts to invalidate claims made in social media posts. The fact filters are initially coarse-grained, based on the original language of the social media posts, and end with fine-grained filters based on the exact time frame in which the posts were uploaded online. This streamlined approach achieved a 0.883 retrieval success rate in the monolingual task while maintaining a scalable efficiency level of processing a social media post per 0.07 seconds.

The SemEval-2025 Task 11, Bridging the Gap in Text-Based Emotion Detection, introduces an emotion recognition challenge spanning over 28 languages. This competition encourages researchers to explore more advanced approaches to address the challenges posed by the diversity of emotional expressions and background variations. It features two tracks: multi-label classification (Track A) and emotion intensity prediction (Track B), covering six emotion categories: anger, fear, joy, sadness, surprise, and disgust.In our work, we systematically explore the benefits of two contrastive learning approaches: sample-based (Contrastive Reasoning Calibration) and generation-based (DPO, SimPO) contrastive learning. The sample-based contrastive approach trains the model by comparing two samples to generate more reliable predictions. The generation-based contrastive approach trains the model to differentiate between correct and incorrect generations, refining its prediction. All models are fine-tuned from LLaMa3-Instruct-8B. Our system achieves 12th place in Track A and 7th place in Track B for English, while ranking among the top-tier performing systems for other languages.

This paper presents a novel multi-agent framework for automated code generation and execution in tabular question answering. Developed for the SemEval-2025 Task 8, our system utilises a structured, multi-agent approach where distinct agents handle dataset extraction, schema identification, prompt engineering, code generation, execution, and prediction. Unlike traditional methods such as semantic parsing-based SQL generation and transformer-based table models such as TAPAS, our approach leverages a large language model-driven code synthesis pipeline using the DeepSeek API. Our system follows a zero-shot inference approach, which generates Python functions that operate directly on structured data. Through the dynamic extraction of dataset schema and intergration into structured prompts, the model comprehension of tabular structures is enhanced, which leads to more precise and interpretable results. Experimental results demonstrate that our system outperforms existing tabular questioning and answering models, achieving an accuracy of 84.67% on DataBench and 86.02% on DataBench-lite, which significantly surpassed the performances of TAPAS (2.68%) and stable-code-3b-GGUF (27%). The source code used in this paper is available at t https://github.com/oseibrefo/semEval25task8