用双空间图网络区分跨语言反义词与同义词，效果优于现有方法。
Bhav-Net: Knowledge Transfer for Cross-Lingual Antonym vs Synonym Distinction via Dual-Space Graph Transformers
- 设计双空间架构，同义词聚类于一空间，反义词在互补空间相似。
- 在8种语言上测试，性能媲美顶尖模型，且可解释性强。
- 适合需要跨语言语义理解的NLP应用，如多语言知识库构建。
跨语言反义词与同义词的区分面临独特计算挑战，因反义词虽属同一语义领域却表达相反含义。本文提出Bhav-Net，一种新型双空间架构，可将复杂多语言模型的知识有效迁移至轻量级语言专用模型，同时保持强跨语言反义-同义区分能力。该方法结合语言特定BERT编码器与图变换器网络，生成不同语义投影：同义词对在某一空间聚集，反义词对在互补空间表现出高相似性。在英语、德语、法语、西班牙语、意大利语、葡萄牙语、荷兰语和俄语共八种语言上进行全面评估，结果表明语义关系建模能有效跨语言迁移。双编码器设计在性能上达到当前最优水平，同时提供可解释的语义表示和良好的跨语言泛化能力。
原文摘要 · Abstract (English)
Antonym vs synonym distinction across multiple languages presents unique computational challenges due to the paradoxical nature of antonymous relationships words that share semantic domains while expressing opposite meanings. This work introduces Bhav-Net, a novel dual-space architecture that enables effective knowledge transfer from complex multilingual models to simpler, language-specific architectures while maintaining robust cross-lingual antonym--synonym distinction capabilities. Our approach combines language-specific BERT encoders with graph transformer networks, creating distinct semantic projections where synonymous pairs cluster in one space while antonymous pairs exhibit high similarity in a complementary space. Through comprehensive evaluation across eight languages (English, German, French, Spanish, Italian, Portuguese, Dutch, and Russian), we demonstrate that semantic relationship modeling transfers effectively across languages. The dual-encoder design achieves competitive performance against state-of-the-art baselines while providing interpretable semantic representations and effective cross-lingual generalization.
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