Name
Abstract | ||
|---|---|---|
Inferring evaluation scores based on human judgments is invaluable compared to using current evaluation metrics which are not suitable for real-time applications e.g. post-editing. However, these judgments are much more expensive to collect especially from expert translators, compared to evaluation based on indicators contrasting source and translation texts. This work introduces a novel approach for quality estimation by combining learnt confidence scores from a probabilistic inference model based on human judgments, with selective linguistic features-based scores, where the proposed inference model infers the credibility of given human ranks to solve the scarcity and inconsistency issues of human judgments. Experimental results, using challenging language-pairs, demonstrate improvement in correlation with human judgments over traditional evaluation metrics. |
Year | Venue | Field |
|---|---|---|
2013 | CoRR | Probabilistic inference,Scarcity,Credibility,Inference,Computer science,Machine translation,Natural language processing,Artificial intelligence,Machine learning |
DocType | Volume | Citations |
Journal | abs/1307.1872 | 0 |
PageRank | References | Authors |
0.34 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ibrahim Sabek | 1 | 27 | 8.03 |
| Noha A. Yousri | 2 | 33 | 5.96 |
| Nagwa M. El-Makky | 3 | 63 | 11.48 |
| Mona Habib | 4 | 0 | 0.34 |