Sharif-STR 在 SemEval-2024 任务 1 中：将 Transformer 作为回归模型用于文本语义关系的细粒度评分
Sharif-STR at SemEval-2024 Task 1: Transformer as a Regression Model for Fine-Grained Scoring of Textual Semantic Relations
AI总结：
本文提出 Sharif-STR，在 SemEval-2024 任务 1 监督赛道中将微调 RoBERTa 作为回归模型进行句子级语义文本相关性评分，验证其跨语言效果并报告英、西、阿语结果。
摘要：
语义文本相关性（Semantic Textual Relatedness, STR）在自然语言处理中具有重要意义，并在多个领域得到应用。传统上，STR 方法依赖于基于知识和统计的方法。然而，随着大型语言模型的出现，研究范式发生转变，并带来了新的方法。本文中，我们通过在 RoBERTa transformer 上利用微调技术，深入研究 Track A（监督式）中的句子级 STR。我们的研究重点是评估该方法在不同语言中的有效性。值得注意的是，研究结果表明 STR 性能取得了有前景的提升，尤其是在拉丁语族语言中。具体而言，结果显示英语方面有显著改进，相关系数达到 0.82，并获得值得肯定的第 19 名。同样，在西班牙语中，我们取得 0.67 的相关系数，位列第 15 名。但我们的方法在阿拉伯语等语言中遇到挑战：在该语言上仅观察到 0.38 的相关系数，最终排名第 20。
英文摘要：
Semantic Textual Relatedness holds significant relevance in Natural Language Processing, finding applications across various domains. Traditionally, approaches to STR have relied on knowledge-based and statistical methods. However, with the emergence of Large Language Models, there has been a paradigm shift, ushering in new methodologies. In this paper, we delve into the investigation of sentence-level STR within Track A (Supervised) by leveraging fine-tuning techniques on the RoBERTa transformer. Our study focuses on assessing the efficacy of this approach across different languages. Notably, our findings indicate promising advancements in STR performance, particularly in Latin languages. Specifically, our results demonstrate notable improvements in English, achieving a correlation of 0.82 and securing a commendable 19th rank. Similarly, in Spanish, we achieved a correlation of 0.67, securing the 15th position. However, our approach encounters challenges in languages like Arabic, where we observed a correlation of only 0.38, resulting in a 20th rank.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
