锻造GEMs：通过基于质量的语料库策展推进希腊语自然语言处理
Forging GEMs: Advancing Greek NLP through Quality-Based Corpus Curation
AI总结：
针对希腊语数据稀缺和架构局限，提出GEMs模型家族，通过质量策展语料库训练，在三个基准上显著提升准确率，最高达3.6%。
摘要：
形态丰富且资源中等水平的语言（如现代希腊语）的自然语言处理进展，一直受到架构停滞、数据稀缺和上下文处理能力有限的阻碍，尤其是在法律等专业领域。在本工作中，我们提出了希腊语嵌入模型（GEMs），这是一个基于Transformer的语言模型新家族，专门通过架构多样性和增强的数据策展来解决这些局限。所提出的模型家族在多个大规模、精心策展的语料库上进行训练，涵盖全面的通用领域数据集和专门的法律语料库，解决了长期阻碍希腊语建模进展的数据稀缺问题。所提出的基于质量的语料库策展方法整合了广泛的预处理流程、复杂的去重策略以及对高质量法律子语料库的定向重复，以增强领域适应性。GEMs家族既包含已建立的架构（RoBERTa和Longformer），也包含先前未应用于希腊语的先进模型（ELECTRA、ConvBERT和ModernBERT），全面覆盖了现代Transformer设计。此外，我们引入了首个针对跨语言法律应用的双语希腊语-英语嵌入模型。在三个核心自然语言理解基准上的全面评估表明，所提出的GEM-RoBERTa和GEM-ConvBERT在统计上显著优于已建立的最先进模型，准确率提升高达3.6%，同时使用Friedman Aligned-Ranks和Finner事后检验进行的统计分析证实了我们的方法在多个评估指标上的优越性。
英文摘要：
The advancement of natural language processing for morphologically rich and moderately-resourced languages like Modern Greek has been hindered by architectural stagnation, data scarcity, and limited context processing capabilities, particularly in specialized domains such as law. In this work, we propose the Greek Embedding Models (GEMs), a new family of transformer-based language models, specifically developed to address these limitations through architectural diversity and enhanced data curation. The proposed family of models are trained on several large-scale, meticulously curated corpora, encompassing both comprehensive general-domain datasets and specialized legal collections, addressing the persistent data scarcity that has impeded Greek language modeling advancement. The proposed quality-based corpus curation methodology incorporates extensive preprocessing pipelines, sophisticated deduplication strategies and targeted repetition of high-quality legal sub-corpora to enhance domain adaptation. The GEMs family comprises both established architectures (RoBERTa and Longformer) and advanced models not previously applied to Greek (ELECTRA, ConvBERT, and ModernBERT), providing comprehensive coverage of modern transformer designs. Additionally, we introduce the first bilingual Greek-English embedding models tailored for cross-lingual legal applications. Comprehensive evaluation across three core natural language understanding benchmarks demonstrates that the proposed GEM-RoBERTa and GEM-ConvBERT achieve statistically significant performance improvements over established state-of-the-art models, with accuracy gains of up to 3.6\% while conducted statistical analysis using Friedman Aligned-Ranks and Finner post-hoc tests confirms the superiority of our approach across multiple evaluation metrics.
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