Name
Title | ||
|---|---|---|
Shallow-Syntax Phrase-Based Translation - Joint versus Factored String-to-Chunk Models. |
Abstract | ||
|---|---|---|
This work extends phrase-based statistical MT (SMT) with shallow syntax dependencies. Two string-to-chunks translation models are proposed: a factored model, which augments phrase-based SMT with layered dependen- cies, and a joint model, that extends the phrase translation table with microtags, i.e. per- word projections of chunk labels. Both rely on n-gram models of target sequences with different granularity: single words, micro- tags, chunks. In particular, n-grams defined over syntactic chunks should model syntac- tic constraints coping with word-group move- ments. Experimental analysis and evaluation conducted on two popular Chinese-English tasks suggest that the shallow-syntax joint- translation model has potential to outperform state-of-the-art phrase-based translation, with a reasonable computational overhead. |
Year | Venue | DocType |
|---|---|---|
2008 | AMTA | Conference |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| mauro cettolo | 1 | 539 | 55.91 |
| marcello federico | 2 | 2420 | 179.56 |
| daniele pighin | 3 | 289 | 18.72 |
| nicola bertoldi | 4 | 1777 | 95.10 |
| fondazione bruno kessler | 5 | 72 | 4.98 |