Name
Abstract | ||
|---|---|---|
Nowadays, there are large amounts of data available to train statistical machine translation systems. However, it is not clear whether all the training data actually help or not. A system trained on a subset of such huge bilingual corpora might outperform the use of all the bilingual data. This paper studies such issues by analysing two training data selection techniques: one based on approximating the probability of an indomain corpus; and another based on infrequent n-gram occurrence. Experimental results not only report significant improvements over random sentence selection but also an improvement over a system trained with the whole available data. Surprisingly, the improvements are obtained with just a small fraction of the data that accounts for less than 0.5% of the sentences. Afterwards, we show that a much larger room for improvement exists, although this is done under non-realistic conditions. |
Year | Venue | Keywords |
|---|---|---|
2012 | EACL | training data,indomain corpus,training data selection technique,statistical machine translation system,bilingual data,whole available data,better translation,significant improvement,random sentence selection,huge bilingual corpus |
Field | DocType | Citations |
Training set,Computer science,Machine translation,Speech recognition,Natural language processing,Artificial intelligence,Sentence | Conference | 10 |
PageRank | References | Authors |
0.53 | 22 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guillem Gascó | 1 | 15 | 2.11 |
| Martha-Alicia Rocha | 2 | 16 | 1.80 |
| Germán Sanchis-Trilles | 3 | 101 | 16.95 |
| Jesús Andrés-Ferrer | 4 | 73 | 7.52 |
| francisco casacuberta | 5 | 1439 | 161.33 |