Supervised and nonlinear alignment of two embedding spaces for dictionary induction in low resourced languages

Abstract

Enabling cross-lingual NLP tasks by leveraging multilingual word embedding has recently attracted much attention. An important motivation is to support lower resourced languages, however, most efforts focus on demonstrating the effectiveness of the techniques using embeddings derived from similar languages to English with large parallel content. In this study, we present a noise tolerant piecewise linear technique to learn a non-linear mapping between two monolingual word embedding vector spaces. We evaluate our approach on inferring bilingual dictionaries. We show that our technique outperforms the state of the art in lower resourced settings with an average improvement of 3.7% for precision @10 across 14 mostly low resourced languages.