Name
Title | ||
|---|---|---|
Towards an Efficient Evolutionary Decoding Algorithm for Statistical Machine Translation |
Abstract | ||
|---|---|---|
In a statistical machine translation system (SMTS), decoding is the process of finding the most likely translation based on a statistical model according to previously learned parameters. This paper proposes a new approach based on evolutionary hybrid algorithms to translate sentences in a specific technical context. The tests are carried out in the context of Spanish and then translated to English. The experimental results validate the performance of our method. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-24694-7_45 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
hybrid algorithm,statistical model | Rule-based machine translation,Example-based machine translation,Evolutionary algorithm,Computer science,Machine translation,Algorithm,Model-based reasoning,Artificial intelligence,Statistical model,Decoding methods,Genetic algorithm,Machine learning | Conference |
Volume | ISSN | Citations |
2972 | 0302-9743 | 3 |
PageRank | References | Authors |
0.43 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eridan Otto | 1 | 5 | 1.19 |
| María Cristina Riff | 2 | 200 | 23.91 |