Name
Abstract | ||
|---|---|---|
New techniques for online adaptation in computer assisted translation are explored and compared to previously existing approaches. Under the online adaptation paradigm, the translation system needs to adapt itself to real-world changing scenarios, where training and tuning may only take place once, when the system is set-up for the first time. For this purpose, post-edit information, as described by a given quality measure, is used as valuable feedback within a dynamic reranking algorithm. Two possible approaches are presented and evaluated. The first one relies on the well-known perceptron algorithm, whereas the second one is a novel approach using the Ridge regression in order to compute the optimum scaling factors within a state-of-the-art SMT system. Experimental results show that such algorithms are able to improve translation quality by learning from the errors produced by the system on a sentence-by-sentence basis. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-19437-5_8 | CICLing (2) |
Keywords | Field | DocType |
ridge regression,translation quality,state-of-the-art smt system,well-known perceptron algorithm,online adaptation,translation system,online adaptation paradigm,dynamic reranking algorithm,quality measure,computer assisted translation | Online learning,Regression,Computer science,Machine translation,Artificial intelligence,Computer-assisted translation,Perceptron,Online adaptation,Machine learning | Conference |
Volume | ISSN | Citations |
6609 | 0302-9743 | 4 |
PageRank | References | Authors |
0.93 | 39 | 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 |