通过集成多种模型提升句子语义相关性预测能力
Tübingen-CL at SemEval-2024 Task 1:Ensemble Learning for Semantic Relatedness Estimation
- 融合统计特征与深度学习模型输出，构建集成系统
- 在SemEval-2024任务中表现优于单一模型
- 适合对语义相关性分析有需求的研究者
本文介绍我们在SemEval-2024 Task 1中的系统，旨在预测句子对的语义相关性。基于语义相关性超越句子相似性的假设，我们的方法致力于识别有助于相关性估计的有效特征。采用集成策略，融合多种系统，包括统计文本特征和深度学习模型的输出，以预测相关性得分。结果表明，语义相关性可从多种来源推断，且集成模型在估计语义相关性方面优于多数单一系统。
原文摘要 · Abstract (English)
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.
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