Name
Abstract | ||
|---|---|---|
Reordering is a challenge to machine translation (MT) systems. In MT, the widely used approach is to apply word based language model (LM) which considers the constituent units of a sentence as words. In speech recognition (SR), some phrase based LM have been proposed. However, those LMs are not necessarily suitable or optimal for reordering. We propose two phrase based LMs which considers the constituent units of a sentence as phrases. Experiments show that our phrase based LMs outperform the word based LM with the respect of perplexity and n-best list re-ranking. |
Year | Venue | Field |
|---|---|---|
2015 | arXiv: Computation and Language | Perplexity,Computer science,Machine translation,Phrase,Artificial intelligence,Natural language processing,Sentence,Language model,Empirical research |
DocType | Volume | Citations |
Journal | abs/1501.05203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| geliang chen | 1 | 0 | 0.34 |
| Jia Xu | 2 | 298 | 36.94 |