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.

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

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PS2 - Machine Translation and translation aids

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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

Yuto Kuroda

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

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