This paper presents a system that applies training and inference approaches for SemEval2026 Task 11 Subtask 1, which focuses on binary classification for content-independent validity reasoning in syllogistic inference. Building on fine-tuning of relatively standard language models, additional approaches were explored, including layer-wise deep supervision and in-context learning. Furthermore, models that had been previously trained on datasets related to logical reasoning were adapted to thetask through additional fine-tuning. Finally, refinement was performed at the inference stage by adjusting the softmax-based decision threshold of the selected model. The experimental results illustrate how model selection, training strategies, and threshold adjustment affect not only validity accuracy but also robustness against plausibility-driven bias, thereby contributing to improved logical integrity.

This paper presents the OZemi team’s submission to SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization.We propose a unified multilingual approach that addresses multiple languages and subtasks efficiently. Our system combines multilingual models with data-level techniques and a class-weighted cross-entropy loss to mitigate data imbalance across languages, subtasks, and categories. Results show consistent performance across languages, achieving macro F1 scores above 70% in most languages for Subtask 1 achieving our highest rank in subtask 1 for Persian (1 out of 44). These results suggest that the proposed framework provides a flexible foundation for multilingual and multi-task polarization analysis.

This paper describes a system developed for SemEval-2026 Task 10 Subtask 2, which focuses on identifying conspiracy beliefs expressed in Reddit comments. The study begins with a comparative analysis of language models fine-tuned on the task data. In addition to fine-tuning, multiple auxiliary techniques were examined, including instruction-based prompting, data augmentation via back-translation, and loss function methods designed to address label imbalance. In the final stage, the inference behavior was further examined by varying the decision threshold applied to the softmax output probabilities. The results highlight how choices made during model selection, training, and inference collectively affect performance, offering empirical insights into the challenges of conspiracy belief detection in social media contexts.

This paper presents the OZemi team’s submission to SemEval-2025 Task 11: Multilingual Emotion Detection and Intensity. Our approach prioritized computational efficiency, leveraging lightweight models that achieved competitive results even for low-resource languages. We addressed data imbalance through data augmentation techniques such as back translation and class balancing. Our system utilized multilingual BERT and machine translation to enhance performance across 35 languages. Despite ranking mid-tier overall, our results demonstrate that relatively simple models can yield adequate performance across diverse linguistic settings. We provide an error analysis of emotion classification challenges, particularly for nuanced expressions such as sarcasm and irony, and discuss the impact of emoji representation on model predictions. Finally, we outline future directions, including improvements in sentiment intensity modeling and the integration of semantic prosody to refine emotion detection.

In this system paper for SemEval-2024 Task10 subtask 1 (ERC), I present my approach to recognizing emotions in Hindi-English codemixed conversations. I train a SpaCy model with English translated data and classify emotions behind Hindi-English code-mixed utterances by using the model and translating them into English. I use machine translation to translate all the data in Hindi-English mixed language into English due to an easy access to existing data for emotion recognition in English. Some additional data in English are used to enhance my model. This English based approach demonstrates a fundamental possibility and potential of simplifying code-mixed language into one major language for emotion recognition.

In this system paper for SemEval-2024 Task4 subtask 2b, I present my approach to identifying propagandistic memes in multiple languages. I firstly establish a baseline for English and then implement the model into other languages (Bulgarian, North Macedonian and Arabic) by using machine translation. Data from other subtasks (subtask 1, subtask 2a) are also used in addition to data for this subtask, and additional data from Kaggle are concatenated to these in order to enhance the model. The results show a high reliability of my English baseline and a room for improvement of its implementation.

In this system paper for SemEval-2024 Task3 subtask 2, I present my simple textual approach to emotion classification and emotion cause analysis in conversations using machine learning and next sentence prediction. I train a SpaCy model for emotion classification and use next sentence prediction with BERT for emotion cause analysis. While speaker names and audio-visual clips are given in addition to text of the conversations, my approach uses textual data only to test my methodology to combine machine learning with next sentence prediction. This paper reveals both strengths and weaknesses of my trial, suggesting a direction of future studies to improve my introductory solution.

In this system paper for SemEval-2024 Task 1 subtask A, we present our approach to evaluating the semantic relatedness of sentence pairs in nine languages. We use a mix of statistical methods combined with fine-tuned BERT transformer models for English and use the same model and machine-translated data for the other languages. This simplistic approach shows consistently reliable scores and achieves above-average rank in all languages.