Name
Abstract | ||
|---|---|---|
We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the training algorithm with a particular schedule. This approach is simple to implement on top of any neural framework or architecture, and consistently outperforms both unadapted and adapted baselines in experiments with two distinct domains and two language pairs. |
Year | Venue | Field |
|---|---|---|
2019 | north american chapter of the association for computational linguistics | Computer science,Domain adaptation,Machine translation,Curriculum,Artificial intelligence,Natural language processing |
DocType | Volume | Citations |
Journal | abs/1905.05816 | 1 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuan Zhang | 1 | 2 | 1.38 |
| Pamela Shapiro | 2 | 1 | 0.34 |
| Gaurav Kumar | 3 | 82 | 5.49 |
| Paul McNamee | 4 | 2 | 1.72 |
| Marine Carpuat | 5 | 587 | 51.99 |
| Kevin Duh | 6 | 819 | 72.94 |