Name
Abstract | ||
|---|---|---|
We introduce an approach to optimize a machine translation (MT) system on multiple metrics simultaneously. Different metrics (e.g. BLEU, TER) focus on different aspects of translation quality; our multi-objective approach leverages these diverse aspects to improve overall quality. Our approach is based on the theory of Pareto Optimality. It is simple to implement on top of existing single-objective optimization methods (e.g. MERT, PRO) and outperforms ad hoc alternatives based on linear-combination of metrics. We also discuss the issue of metric tunability and show that our Pareto approach is more effective in incorporating new metrics from MT evaluation for MT optimization. |
Year | Venue | Keywords |
|---|---|---|
2012 | ACL | different aspect,different metrics,multiple metrics,multiple objective,new metrics,pareto approach,multi-objective approach,pareto optimality,mt evaluation,machine translation,mt optimization |
Field | DocType | Volume |
BLEU,Computer science,Machine translation,Artificial intelligence,Pareto principle,Machine learning | Conference | P12-1 |
Citations | PageRank | References |
6 | 0.46 | 33 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin Duh | 1 | 819 | 72.94 |
| Katsuhito Sudoh | 2 | 326 | 34.44 |
| Xianchao Wu | 3 | 64 | 6.62 |
| Hajime Tsukada | 4 | 449 | 29.46 |
| Masaaki Nagata | 5 | 573 | 77.86 |