Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation

Abstract

We propose a method to distill language-agnostic meaning embeddings from multilingual sentence encoders for unsupervised quality estimation of machine translation. Our method facilitates that the meaning embeddings focus on semantics by adversarial training that attempts to eliminate language-specific information. Experimental results on unsupervised quality estimation reveal that our method achieved higher correlations with human evaluations.