Bhav-Net：通过双空间图变换器实现跨语言反义词与同义词区分的知识迁移

跨多语言的反义词与同义词区分 presents unique computational challenges due to the paradoxical nature of antonymous relationships words that share semantic domains while expressing opposite meanings. 本 work introduces Bhav-Net，一种 novel dual-space architecture，它 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.