Quality Estimation and Translation Metrics via Pre-trained Word and Sentence Embeddings
Elizaveta Yankovskaya, Andre Tättar, Mark Fishel · 2019
We propose the use of pre-trained embeddings as features of a regression model for sentencelevel quality estimation of machine translation.In our work we combine freely available BERT and LASER multilingual embeddings to train a neural-based regression model.In the second proposed method we use as an input features not only pre-trained embeddings, but also log probability of any machine translation (MT) system.Both methods are applied to several language pairs and are evaluated both as a classical quality estimation system (predicting the HTER score) as well as an MT metric (predicting human judgements of translation quality).