Name
Abstract | ||
|---|---|---|
This paper describes a lexical trigger model for statistical machine translation. We present various methods using triplets incorporating long-distance dependencies that can go beyond the local context of phrases or n-gram based language models. We evaluate the presented methods on two translation tasks in a reranking framework and compare it to the related IBM model 1. We show slightly improved translation quality in terms of BLEU and TER and address various constraints to speed up the training based on Expectation-Maximization and to lower the overall number of triplets without loss in translation performance. |
Year | Venue | Keywords |
|---|---|---|
2008 | EMNLP | translation task,statistical machine translation,related ibm model,lexical trigger model,language model,various constraint,triplet lexicon model,local context,various method,translation performance,improved translation quality,expectation maximization |
Field | DocType | Volume |
Rule-based machine translation,Example-based machine translation,Computer science,Evaluation of machine translation,Machine translation,Synchronous context-free grammar,Machine translation software usability,Transfer-based machine translation,Natural language processing,Artificial intelligence,Machine learning,Language model | Conference | D08-1 |
Citations | PageRank | References |
26 | 0.94 | 71 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sasa Hasan | 1 | 245 | 17.35 |
| Juri Ganitkevitch | 2 | 659 | 32.71 |
| Hermann Ney | 3 | 14178 | 1506.93 |
| Jesús Andrés-Ferrer | 4 | 73 | 7.52 |