Unsupervised Quality Estimation for Neural Machine Translation
This addresses the challenge of making machine translation more practical in real-world applications by providing a cost-effective quality estimation method, though it is incremental as it builds on existing uncertainty quantification techniques.
The paper tackles the problem of quality estimation for neural machine translation by proposing an unsupervised approach that eliminates the need for expert annotations and extensive training, achieving correlation with human judgments that rivals state-of-the-art supervised models.
Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time. Existing approaches require large amounts of expert annotated data, computation and time for training. As an alternative, we devise an unsupervised approach to QE where no training or access to additional resources besides the MT system itself is required. Different from most of the current work that treats the MT system as a black box, we explore useful information that can be extracted from the MT system as a by-product of translation. By employing methods for uncertainty quantification, we achieve very good correlation with human judgments of quality, rivalling state-of-the-art supervised QE models. To evaluate our approach we collect the first dataset that enables work on both black-box and glass-box approaches to QE.