Name
Title | ||
|---|---|---|
Online adaptation strategies for statistical machine translation in post-editing scenarios |
Abstract | ||
|---|---|---|
One of the most promising approaches to machine translation consists in formulating the problem by means of a pattern recognition approach. By doing so, there are some tasks in which online adaptation is needed in order to adapt the system to changing scenarios. In the present work, we perform an exhaustive comparison of four online learning algorithms when combined with two adaptation strategies for the task of online adaptation in statistical machine translation. Two of these algorithms are already well-known in the pattern recognition community, such as the perceptron and passive-aggressive algorithms, but here they are thoroughly analyzed for their applicability in the statistical machine translation task. In addition, we also compare them with two novel methods, i.e., Bayesian predictive adaptation and discriminative ridge regression. In statistical machine translation, the most successful approach is based on a log-linear approximation to a posteriori distribution. According to experimental results, adapting the scaling factors of this log-linear combination of models using discriminative ridge regression or Bayesian predictive adaptation yields the best performance. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.patcog.2012.01.011 | Pattern Recognition |
Keywords | Field | DocType |
log-linear approximation,bayesian predictive adaptation yield,statistical machine translation,online adaptation strategy,statistical machine translation task,online adaptation,discriminative ridge regression,bayesian predictive adaptation,log-linear combination,machine translation,adaptation strategy,post-editing scenario,adaptation,natural language processing | Regression,Pattern recognition,Computer science,Machine translation,Interactive machine translation,A priori and a posteriori,Artificial intelligence,Perceptron,Discriminative model,Online adaptation,Machine learning,Bayesian probability | Journal |
Volume | Issue | ISSN |
45 | 9 | 0031-3203 |
Citations | PageRank | References |
21 | 0.78 | 34 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pascual Martínez-Gómez | 1 | 61 | 7.36 |
| Germán Sanchis-Trilles | 2 | 101 | 16.95 |
| francisco casacuberta | 3 | 1439 | 161.33 |