语义文本相关性的多语言评估
Multilingual Evaluation of Semantic Textual Relatedness
AI总结：
针对现有NLP研究多聚焦英语、多语言语义文本相关性（STR）研究不足的问题，本文依托SemEval-2024共享任务，在三种学习范式下探究多语言模型在四种语言STR任务上的表现，取得可观结果，为低资源语言相关研究提供启发。
摘要：
在线内容的爆炸式增长要求稳健的自然语言处理（NLP）技术能够捕捉不同语言中的细微含义与文化语境。语义文本相关性（Semantic Textual Relatedness，STR）超越了表面的词汇重叠，会考量语言要素以及主题、情感、视角等非语言因素。尽管STR作用关键，但以往的NLP研究主要聚焦于英语，限制了其跨语言适用性。为弥补这一空白，本文致力于捕捉句子间超越简单词汇重叠的深层关联。我们突破以英语为中心的NLP研究范式，针对马拉地语、印地语、西班牙语和英语开展STR研究，挖掘其在信息检索、机器翻译等领域的应用潜力。依托SemEval-2024共享任务，我们在监督、无监督和跨语言三种学习范式下探究了多种语言模型的表现。我们的综合方法取得了可观的结果，验证了所提方法的有效性。本研究不仅旨在展示相关成果，更希望推动多语言STR领域的进一步研究，尤其是低资源语言方向的探索。
英文摘要：
The explosive growth of online content demands robust Natural Language Processing (NLP) techniques that can capture nuanced meanings and cultural context across diverse languages. Semantic Textual Relatedness (STR) goes beyond superficial word overlap, considering linguistic elements and non-linguistic factors like topic, sentiment, and perspective. Despite its pivotal role, prior NLP research has predominantly focused on English, limiting its applicability across languages. Addressing this gap, our paper dives into capturing deeper connections between sentences beyond simple word overlap. Going beyond English-centric NLP research, we explore STR in Marathi, Hindi, Spanish, and English, unlocking the potential for information retrieval, machine translation, and more. Leveraging the SemEval-2024 shared task, we explore various language models across three learning paradigms: supervised, unsupervised, and cross-lingual. Our comprehensive methodology gains promising results, demonstrating the effectiveness of our approach. This work aims to not only showcase our achievements but also inspire further research in multilingual STR, particularly for low-resourced languages.
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。
