多思维在SemEval-2025任务7中的应用：通过多源对齐实现跨语言事实核查声明检索
MultiMind at SemEval-2025 Task 7: Crosslingual Fact-Checked Claim Retrieval via Multi-Source Alignment
AI总结：
TriAligner通过多源对齐和对比学习，实现跨语言事实核查声明的高效检索，提升多语言环境下的信息准确性。
摘要：
本文介绍了我们为SemEval-2025任务7：多语言和跨语言事实核查声明检索所设计的系统。在信息快速传播的时代，有效的事实核查变得越来越重要。我们引入了TriAligner，一种新颖的方法，利用双编码架构与对比学习，并结合不同模态下的本民族语言和英语翻译。我们的方法通过学习不同来源在对齐中的相对重要性，有效检索多语言声明。为了增强鲁棒性，我们使用大型语言模型进行高效的预处理和增强，并结合硬负样本采样以提高表示学习。我们在单语言和跨语言基准上评估了我们的方法，展示了在检索准确性和事实核查性能上显著优于基线的结果。
英文摘要：
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 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
