Name
Abstract | ||
|---|---|---|
We present TESLA-M and TESLA, two novel automatic machine translation evaluation metrics with state-of-the-art performances. TESLA-M builds on the success of METEOR and MaxSim, but employs a more expressive linear programming framework. TESLA further exploits parallel texts to build a shallow semantic representation. We evaluate both on the WMT 2009 shared evaluation task and show that they outperform all participating systems in most tasks. |
Year | Venue | Keywords |
|---|---|---|
2010 | WMT@ACL | shallow semantic representation,parallel text,evaluation metrics,translation evaluation,linear-programming-based analysis,expressive linear programming framework,shared evaluation task,state-of-the-art performance,novel automatic machine translation |
Field | DocType | Citations |
Rule-based machine translation,Example-based machine translation,Computer science,Machine translation,Exploit,Linear programming,Artificial intelligence,Natural language processing,Semantic representation | Conference | 22 |
PageRank | References | Authors |
1.08 | 20 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| chang liu | 1 | 87 | 6.78 |
| Daniel Dahlmeier | 2 | 460 | 29.67 |
| Hwee Tou Ng | 3 | 4092 | 300.40 |