wmt20-mlqe-task1

This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task.
Task 1 uses Wikipedia data for 6 language pairs that includes high-resource English--German (En-De) and English--Chinese (En-Zh), medium-resource Romanian--English (Ro-En) and Estonian--English (Et-En), and low-resource Sinhalese--English (Si-En) and Nepalese--English (Ne-En), as well as a dataset with a combination of Wikipedia articles and Reddit articles for Russian-English (En-Ru). The datasets were collected by translating sentences sampled from source language articles using state-of-the-art NMT models built using the fairseq toolkit and annotated with Direct Assessment (DA) scores by professional translators. Each sentence was annotated following the FLORES setup, which presents a form of DA, where at least three professional translators rate each sentence from 0-100 according to the perceived translation quality. DA scores are standardised using the z-score by rater. Participating systems are required to score sentences according to z-standardised DA scores.