Name
Abstract | ||
|---|---|---|
This paper describes our system of the sentence-level and word-level Quality Estimation Shared Task of WMT20. Our system is based on the QE Brain, and we simply enhance it by injecting noise at the target side. And to obtain the deep bi-directional information, we use a masked language model at the target side instead of two single directional decoders. Meanwhile, we try to use the extra QE data from the WMT17 and WMT19 to improve our system’s performance. Finally, we ensemble the features or the results from different models to get our best results. Our system finished fifth in the end at sentence-level on both EN-ZH and EN-DE language pairs. |
Year | Venue | DocType |
|---|---|---|
2020 | WMT@EMNLP | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qu Cui | 1 | 0 | 1.01 |
| Xiang Geng | 2 | 9 | 2.85 |
| Shujian Huang | 3 | 158 | 28.78 |
| Jiajun Chen | 4 | 0 | 2.03 |