| Abstract | Then we employ a hybrid method combining RBMT and SMT systems to fill up the data gap for pivot translation, where the source-pivot and pivot-target corpora are independent. |
| Experiments | 5.3 Results by Using SMT Systems |
| Experiments | Table 3: CRR/ASR translation results by using SMT systems |
| Experiments | 5.4 Results by Using both RBMT and SMT Systems |
| Introduction | Unfortunately, large quantities of parallel data are not readily available for some languages pairs, therefore limiting the potential use of current SMT systems . |
| Introduction | Experimental results show that (l) the performances of the three pivot methods are comparable when only SMT systems are used. |
| Pivot Methods for Phrase-based SMT | Where L is the number of features used in SMT systems . |
| Pivot Methods for Phrase-based SMT | This can be achieved by translating the pivot sentences in source-pivot corpus to target sentences with the pivot-target SMT system . |
| Pivot Methods for Phrase-based SMT | The other is to obtain source translations for the target sentences in the pivot-target corpus using the pivot-source SMT system . |
| Using RBMT Systems for Pivot Translation | Since it is easy to obtain monolingual corpora than bilingual corpora, we use RBMT systems to translate the available monolingual corpora to obtain synthetic bilingual corpus, which are added to the training data to improve the performance of SMT systems . |
| Discussion | MERT and MBR decoding are popular techniques for incorporating the final evaluation metric into the development of SMT systems . |
| Introduction | These two techniques were originally developed for N -best lists of translation hypotheses and recently extended to translation lattices (Macherey et al., 2008; Tromble et al., 2008) generated by a phrase-based SMT system (Och and Ney, 2004). |
| Introduction | SMT systems based on synchronous context free grammars (SCFG) (Chiang, 2007; Zollmann and Venugopal, 2006; Galley et al., 2006) have recently been shown to give competitive performance relative to phrase-based SMT. |
| Translation Hypergraphs | A translation lattice compactly encodes a large number of hypotheses produced by a phrase-based SMT system . |
| Translation Hypergraphs | The corresponding representation for an SMT system based on SCFGs (e.g. |
| Abstract | Current SMT systems usually decode with single translation models and cannot benefit from the strengths of other models in decoding phase. |
| Background | Most SMT systems approximate the summation over all possible derivations by using l-best derivation for efficiency. |
| Background | By now, most current SMT systems , adopting either max-derivation decoding or max-translation decoding, have only used single models in decoding phase. |