This paper presents the MasonTigers entry to the SemEval-2024 Task 1 - Semantic Textual Relatedness. The task encompasses supervised (Track A), unsupervised (Track B), and cross-lingual (Track C) approaches across 14 different languages. MasonTigers stands out as one of the two teams who participated in all languages across the three tracks. Our approaches achieved rankings ranging from 11th to 21st in Track A, from 1st to 8th in Track B, and from 5th to 12th in Track C. Adhering to the task-specific constraints, our best performing approaches utilize ensemble of statistical machine learning approaches combined with language-specific BERT based models and sentence transformers.

本研究论文介绍了MasonTigers参与SemEval-2024任务1的成果，该任务涵盖了14种不同语言的监督学习、无监督学习和跨语言方法。MasonTigers是两个参与了所有语言的团队之一，在三个任务中取得了从第11到第21名的排名（Track A）、第1到第8名的排名（Track B）和第5到第12名的排名（Track C）。我们的最佳方法结合了统计机器学习方法的集成以及语言特定的BERT模型和句子转换器。

MasonTigers参加SemEval-2024任务1：语义文本关联的集成方法