Name
Abstract | ||
|---|---|---|
Confidence Estimation has been extensively used in Speech Recognition and now it is also being applied in Statistical Machine Translation. Its basic goal is to estimate a confidence measure for each word in a given hypothesis, in order to locate those words, if any, that are likely to be incorrectly recognised or translated. It can be seen as a two-class pattern recognition problem in which each hypothesized word is transformed into a feature vector and then classified as either correct or incorrect. This view provides a solid, well-know framework, within which accurate dichotomizers (two-class classifiers) can be derived. In this paper, we study the performance of certain pattern features along with a smoothed Naive Bayes dichotomizer. Good empirical results are reported on a translation task of technical manuals. |
Year | Venue | DocType |
|---|---|---|
2007 | MTSummit | Conference |
Citations | PageRank | References |
5 | 0.54 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alberto Sanchis | 1 | 73 | 7.67 |
| alfons juan | 2 | 572 | 61.45 |
| Enrique Vidal | 3 | 1096 | 85.46 |
| departament de sistemes informatics | 4 | 5 | 0.54 |