Name
Abstract | ||
|---|---|---|
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE). The dataset contains seven language pairs, with human labels for 9,000 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. |
Year | Venue | DocType |
|---|---|---|
2022 | International Conference on Language Resources and Evaluation (LREC) | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marina Fomicheva | 1 | 3 | 2.92 |
| Shuo Sun | 2 | 0 | 1.35 |
| Erick Fonseca | 3 | 0 | 1.01 |
| Frédéric Blain | 4 | 18 | 5.94 |
| Vishrav Chaudhary | 5 | 8 | 8.26 |
| Francisco Guzmán | 6 | 54 | 13.51 |
| Nina Lopatina | 7 | 0 | 0.68 |
| lucia specia | 8 | 1217 | 122.84 |
| André F. T. Martins | 9 | 801 | 52.10 |