Name
Title | ||
|---|---|---|
The impact of parse quality on syntactically-informed statistical machine translation |
Abstract | ||
|---|---|---|
We investigate the impact of parse quality on a syntactically-informed statistical machine translation system applied to technical text. We vary parse quality by varying the amount of data used to train the parser. As the amount of data increases, parse quality improves, leading to improvements in machine translation output and results that significantly outperform a state-of-the-art phrasal baseline. |
Year | Venue | Keywords |
|---|---|---|
2006 | EMNLP | machine translation output,technical text,parse quality,state-of-the-art phrasal baseline,syntactically-informed statistical machine translation,data increase,machine translation,quality improvement |
Field | DocType | Volume |
Evaluation of machine translation,Computer science,Machine translation,Machine translation system,Speech recognition,Natural language processing,Artificial intelligence,Parsing | Conference | W06-16 |
ISBN | Citations | PageRank |
1-932432-73-6 | 28 | 1.20 |
References | Authors | |
15 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chris Quirk | 1 | 70 | 4.97 |
| Simon Corston-Oliver | 2 | 349 | 25.25 |