Name
Abstract | ||
|---|---|---|
We propose a simple training regime that can improve the extrinsic performance of a parser, given only a corpus of sentences and a way to automatically evaluate the extrinsic quality of a candidate parse. We apply our method to train parsers that excel when used as part of a reordering component in a statistical machine translation system. We use a corpus of weakly-labeled reference reorderings to guide parser training. Our best parsers contribute significant improvements in subjective translation quality while their intrinsic attachment scores typically regress. |
Year | Venue | Keywords |
|---|---|---|
2011 | EMNLP | simple training regime,extrinsic quality,machine translation reordering,parser training,statistical machine translation system,intrinsic attachment score,candidate parse,best parsers,subjective translation quality,extrinsic performance,reordering component |
Field | DocType | Volume |
LR parser,Computer science,Machine translation,Machine translation system,Simple LR parser,GLR parser,Speech recognition,Natural language processing,Artificial intelligence,Parsing,Parser combinator | Conference | D11-1 |
Citations | PageRank | References |
31 | 0.85 | 37 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jason Katz-Brown | 1 | 83 | 3.00 |
| Slav Petrov | 2 | 2405 | 107.56 |
| Ryan McDonald | 3 | 4653 | 245.25 |
| Franz Josef Och | 4 | 8864 | 606.01 |
| David Talbot | 5 | 59 | 2.05 |
| Hiroshi Ichikawa | 6 | 54 | 1.59 |
| Masakazu Seno | 7 | 149 | 8.14 |
| Hideto Kazawa | 8 | 709 | 37.48 |