Abstract

Quality estimation (QE) of machine translation (MT) systems is a task of growing importance. It reduces the cost of post-editing, allowing machine-translated text to be used in formal occasions. In this work, we describe our submission system in WMT 2019 sentence-level QE task. We mainly explore the utilization of pre-trained translation models in QE and adopt a bi-directional translation-like strategy. The strategy is similar to ELMo, but additionally conditions on source sentences. Experiments on WMT QE dataset show that our strategy, which makes the pre-training slightly harder, can bring improvements for QE. In WMT-2019 QE task, our system ranked in the second place on En-De NMT dataset and the third place on En-Ru NMT dataset.

Highlights

  • The quality of machine translation systems have been significantly improved over the past few years (Chatterjee et al, 2018), especially with the development of neural machine translation (NMT) models (Sutskever et al, 2014; Bahdanau et al, 2014)

  • We can see that our SOURCE model significantly outperforms the state-of-theart single model from the previous year (Bilingual Expert) and is comparable to the ensemble model from JXNU

  • WMT-18 En-De-NMT We evaluate our model through CodaLab, which is recommended by the host

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Summary

Introduction

The quality of machine translation systems have been significantly improved over the past few years (Chatterjee et al, 2018), especially with the development of neural machine translation (NMT) models (Sutskever et al, 2014; Bahdanau et al, 2014). Despite such inspiring improvements, some machine translated texts are still error-prone and unreliable compared to those by professional humans. The task of QE aims to evaluate the output of a machine translation system without access to reference translations.

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