Learning Tibetan-Chinese Cross-Lingual Word Embeddings

Abstract

The idea of Word Embedding is based on the semantic distribution hypothesis of the linguist Harris (1954), who believes that words of the same semantics are distributed in similar contexts. Learning of vector-space word embeddings is a technique of central importance in natural language processing. In recent years, cross-lingual word vectors have received more and more attention. Cross-lingual word vectors enable knowledge transfer between different languages, the most important It is this transfer that can take place between resource-rich and low-resource languages. This paper uses Tibetan and Chinese Wikipedia corpus to train monolingual word vectors, mainly using the fastText word vector training method, and the two monolingual word vectors are analyzed by CCA correlation, thus obtaining Tibetan-Chinese cross-lingual word vectors. In the experiment, we evaluated the resulting word representations on standard lexical semantic evaluation tasks and the results show that this method has a certain improvement on the semantic representation of the word vector.