Name
Abstract | ||
|---|---|---|
We propose a flexible and efficient domain adaptation method that yields consistent improvements in machine translation (for 11 language pairs). The idea is to decompose the word alignment process into two steps, model training and alignment inference, and perform Bayesian adaptation on the latter. This modularity allows one to incorporate out-of-domain data without the need to modify existing training algorithms. We show how ideas in sequential Bayesian methods can be naturally applied to the word alignment problem and demonstrate various positive results on EMEA and NIST datasets. |
Year | Venue | DocType |
|---|---|---|
2011 | MTSummit | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin Duh | 1 | 819 | 72.94 |
| Katsuhito Sudoh | 2 | 326 | 34.44 |
| Tomoharu Iwata | 3 | 824 | 65.87 |
| Hajime Tsukada | 4 | 449 | 29.46 |