T\"ubingen-CL at SemEval-2024 Task 1:Ensemble Learning for Semantic Relatedness Estimation

TL;DR

This paper presents an ensemble learning system for SemEval-2024 Task 1 that combines statistical features and deep learning outputs to improve semantic relatedness estimation of sentence pairs.

Contribution

It introduces an ensemble approach integrating diverse features and models, advancing the accuracy of semantic relatedness prediction.

Findings

Ensemble models outperform individual systems.

Semantic relatedness can be inferred from multiple sources.

Deep learning features enhance relatedness estimation.

Abstract

The paper introduces our system for SemEval-2024 Task 1, which aims to predict the relatedness of sentence pairs. Operating under the hypothesis that semantic relatedness is a broader concept that extends beyond mere similarity of sentences, our approach seeks to identify useful features for relatedness estimation. We employ an ensemble approach integrating various systems, including statistical textual features and outputs of deep learning models to predict relatedness scores. The findings suggest that semantic relatedness can be inferred from various sources and ensemble models outperform many individual systems in estimating semantic relatedness.