Cross-lingual word embeddings can be applied to several natural language
processing applications across multiple languages. Unlike prior works that use
word embeddings based on the Euclidean space, this short paper presents a
simple and effective cross-lingual Word2Vec model that adapts to the Poincaré
ball model of hyperbolic space to learn unsupervised cross-lingual word
representations from a German-English parallel corpus. It has been shown that
hyperbolic embeddings can capture and preserve hierarchical relationships. We
evaluate the model on both hypernymy and analogy tasks. The proposed model
achieves comparable performance with the vanilla Word2Vec model on the
cross-lingual analogy task, the hypernymy task shows that the cross-lingual
Poincaré Word2Vec model can capture latent hierarchical structure from free
text across languages, which are absent from the Euclidean-based Word2Vec
representations. Our results show that by preserving the latent hierarchical
information, hyperbolic spaces can offer better representations for
cross-lingual embeddings.

本文介绍了一种将超球面上的 Poincaré 模型与 Word2Vec 模型相结合的方法，用于从德 - 英平行语料库中学习跨语言单词表示，并且实验证明通过保留潜在的层级信息，在超球面上的表示法可以提供更好的跨语言嵌入。