Name
Abstract | ||
|---|---|---|
This paper presents the JHU-Microsoft joint submission for WMT 2021 quality estimation shared task. We only participate in Task 2 (post-editing effort estimation) of the shared task, focusing on the target-side word-level quality estimation. The techniques we experimented with include Levenshtein Transformer training and data augmentation with a combination of forward, backward, round-trip translation, and pseudo post-editing of the MT output. We demonstrate the competitiveness of our system compared to the widely adopted OpenKiwi-XLM baseline. Our system is also the top-ranking system on the MT MCC metric for the English-German language pair. |
Year | Venue | DocType |
|---|---|---|
2021 | WMT@EMNLP | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuoyang Ding | 1 | 0 | 0.68 |
| Marcin Junczys-Dowmunt | 2 | 312 | 24.24 |
| Matt Post | 3 | 414 | 35.72 |
| Christian Federmann | 4 | 262 | 27.49 |
| Philipp Koehn | 5 | 7684 | 431.77 |