Mohammad Ali Ali Panah


2025

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MultiMind at SemEval-2025 Task 7: Crosslingual Fact-Checked Claim Retrieval via Multi-Source Alignment
Mohammad Mahdi Abootorabi | Alireza Ghahramani Kure | Mohammadali Mohammadkhani | Sina Elahimanesh | Mohammad Ali Ali Panah
Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)

This paper presents our system for SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval. In an era where misinformation spreads rapidly, effective fact-checking is increasingly critical. We introduce {textbf{TriAligner}}, a novel approach that leverages a dual-encoder architecture with contrastive learning and incorporates both native and English translations across different modalities. Our method effectively retrieves claims across multiple languages by learning the relative importance of different sources in alignment. To enhance robustness, we employ efficient data preprocessing and augmentation using large language models while incorporating hard negative sampling to improve representation learning. We evaluate our approach on monolingual and crosslingual benchmarks, demonstrating significant improvements in retrieval accuracy and fact-checking performance over baselines.