NJU Submissions for the WMT19 Quality Estimation Shared Task
Hou Qi · 2019
In this paper, we describe the submissions of the team from Nanjing University for the WMT19 sentence-level Quality Estimation (QE) shared task on English-German language pair.We develop two approaches based on a two-stage neural QE model consisting of a feature extractor and a quality estimator.More specifically, one of the proposed approaches employs the translation knowledge between the two languages from two different translation directions; while the other one employs extra monolingual knowledge from both source and target sides, obtained by pretraining deep self-attention networks.To efficiently train these two-stage models, a joint learning training method is applied.Experiments show that the ensemble model of the above two models achieves the best results on the benchmark dataset of the WMT17 sentence-level QE shared task and obtains competitive results in WMT19, ranking 3rd out of 10 submissions.