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.

翻译：本文介绍了MasonTigers团队参与SemEval-2024任务1（语义文本相关性）的成果。该任务涵盖监督式（Track A）、无监督式（Track B）和跨语言（Track C）三类方法，涉及14种不同语言。MasonTigers是唯一在三个子任务中均参与所有语言评估的两个团队之一。我们的方法在Track A中排名第11至21位，Track B中排名第1至8位，Track C中排名第5至12位。遵循任务特定约束，我们最佳表现的方法采用统计机器学习集成策略，并结合基于特定语言的BERT模型和句子转换器（sentence transformers）。