MasonTigers at SemEval-2024 Task 1: An Ensemble Approach for Semantic Textual Relatedness
This is an incremental contribution for NLP researchers, focusing on improving performance in a specific benchmark competition.
The paper tackled the SemEval-2024 Task 1 on semantic textual relatedness across multiple languages and tracks, achieving rankings from 1st to 21st, with best results using ensemble methods combining statistical machine learning and BERT-based models.
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.