MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset
This dataset addresses the need for standardized resources in multilingual quality estimation and post-editing research, though it is incremental as it builds on existing data collection efforts.
The authors introduced MLQE-PE, a new dataset for machine translation quality estimation and automatic post-editing, containing eleven language pairs with up to 10,000 human-labeled translations per pair, including sentence-level assessments and word-level labels.
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE). The dataset contains eleven language pairs, with human labels for up to 10,000 translations per language pair in the following formats: sentence-level direct assessments and post-editing effort, and word-level good/bad labels. It also contains the post-edited sentences, as well as titles of the articles where the sentences were extracted from, and the neural MT models used to translate the text.