@inproceedings{abootorabi-etal-2025-multimind,
    title = "{M}ulti{M}ind at {S}em{E}val-2025 Task 7: Crosslingual Fact-Checked Claim Retrieval via Multi-Source Alignment",
    author = "Abootorabi, Mohammad Mahdi  and
      Ghahramani Kure, Alireza  and
      Mohammadkhani, Mohammadali  and
      Elahimanesh, Sina  and
      Ali Panah, Mohammad Ali",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.303/",
    pages = "2325--2335",
    ISBN = "979-8-89176-273-2",
    abstract = "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."
}