Name
Abstract | ||
|---|---|---|
This paper describes our work with the data distributed for the WMT'12 Confidence Estimation shared task. Our contribution is twofold: i) we first present an analysis of the data which highlights the difficulty of the task and motivates our approach; ii) we show that using non-linear models, namely random forests, with a simple and limited feature set, succeeds in modeling the complex decisions required to assess translation quality and achieves results that are on a par with the second best results of the shared task. |
Year | Venue | Keywords |
|---|---|---|
2012 | WMT@NAACL-HLT | non-linear model,translation quality,confidence estimation,complex decision,shared task,best result,limited feature set,random forest |
Field | DocType | Citations |
Data mining,Computer science,Feature set,Non linear model,Artificial intelligence,Random forest,Machine learning | Conference | 2 |
PageRank | References | Authors |
0.39 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Zhuang | 1 | 254 | 13.88 |
| Guillaume Wisniewski | 2 | 118 | 27.53 |
| François Yvon | 3 | 941 | 102.51 |