Unsupervised Multilingual Word Embeddings
Xilun Chen, Claire Cardie · 2018
Multilingual Word Embeddings (MWEs) represent words from multiple languages in a single distributional vector space.Unsupervised MWE (UMWE) methods acquire multilingual embeddings without cross-lingual supervision, which is a significant advantage over traditional supervised approaches and opens many new possibilities for low-resource languages.Prior art for learning UMWEs, however, merely relies on a number of independently trained Unsupervised Bilingual Word Embeddings (UBWEs) to obtain multilingual embeddings.These methods fail to leverage the interdependencies that exist among many languages.To address this shortcoming, we propose a fully unsupervised framework for learning MWEs 1 that directly exploits the relations between all language pairs.Our model substantially outperforms previous approaches in the experiments on multilingual word translation and cross-lingual word similarity.In addition, our model even beats supervised approaches trained with cross-lingual resources.