Cross-Lingual Word Representations via Spectral Graph Embeddings

Cross-lingual word embeddings are used for cross-lingual information retrieval or domain adaptations. In this paper, we extend Eigenwords, spectral monolingual word embeddings based on canonical correlation analysis (CCA), to crosslingual settings with sentence-alignment. For incorporating cross-lingual information, CCA is replaced with its generalization based on the spectral graph embeddings. The proposed method, which we refer to as Cross-Lingual Eigenwords (CL-Eigenwords), is fast and scalable for computing distributed representations of words via eigenvalue decomposition. Numerical experiments of English-Spanish word translation tasks show that CLEigenwords is competitive with stateof-the-art cross-lingual word embedding methods.