Food safety incidents cause serious threats to public health, requiring efficient detection systems. Thisstudy contributes to SemEval 2025 Task 9: Food Hazard Detection by leveraging insights from existing literature and using multiple BERT-based models for multi-label classification of food hazards andproduct categories. Using a dataset of food recall notifications, we applied preprocessing techniquesto prepare data and address challenges like class imbalance. Experimental results show strong hazardclassification performance on ensembled models such as DistilBERT, SciBERT, and DeBERTa but highlight product classification variability. Building on Nancy et al. and Madry et al.’s work, we explored strategies like ensemble modeling and data augmentation to improve accuracy and explainability, paving the way for scalable food safety solutions.

This paper presents NarrativeNexus’ participation in SemEval-2025 Task 10 on fine-grained entity framing and narrative extraction. Our approach utilizes BART, a transformer-based encoder-decoder model, fine-tuned for sequence classification and text generation.For Subtask 1, we employed a BART-based sequence classifier to identify and categorize named entities within news articles, mapping them to predefined roles such as protagonists, antagonists, and innocents. In Subtask 3, we leveraged a text-to-text generative approach to generate justifications for dominant narratives.Our methodology included hyperparameter tuning, data augmentation, and ablation studies to assess model components. NarrativeNexus achieved 18th place in Subtask 1 and 10th in Subtask 3 on the English dataset. Our findings highlight the strengths of pre-trained transformers in structured content analysis while identifying areas for future improvements in nuanced entity framing.

SemEval Task 6: Promise Eval, was designed to evaluate a company’s adherence to its ESG commitments. Using Natural Language Processing (NLP) and Deep Learning techniques, the task involves analyzing ESG reports to identify, classify, and verify corporate promises. The verification process follows a structured pipeline with four subtasks: Promise Classification, Evidence Verification, Evidence Classification, and Timeline Verification. These subtasks ensure that identified promises are well-defined, supported by credible evidence, and time-bound.For model implementation, BERT was initially used for most of the classification tasks but was later replaced with DeBERTa, which improved performance due to its superior contextual understanding. To enhance model generalization, contrastive learning was applied alongside standard classification loss, helping the model differentiate between positive and negative examples. Oversampling techniques were used to address class imbalance issues, particularly for the Misleading evidence category. For timeline verification, BART was chosen initially but then shifted to DeBERTa again, as it better captures sequential dependencies in text.The dataset consists of ESG reports containing labeled promise statements, evidence snippets, and timeline information. The data was preprocessed by tokenizing text, handling imbalanced classes through oversampling, and incorporating domain-specific embeddings to improve understanding.By implementing these techniques, the research aims to provide a transparent and accountable framework for assessing corporate promises, ensuring that companies are held accountable for their ESG commitments.