| Experimental Setup | NIST 13.01 12.95 12.69 Match 27.91 27.66 26.38 Ling. |
| Experimental Setup | Model BLEU 0.764 0.759 0.747 NIST 13.18 13.14 13.01 |
| Experimental Setup | use several standard measures: a) exact match: how often does the model select the original corpus sentence, b) BLEU: n-gram overlap between top-ranked and original sentence, c) NIST : modification of BLEU giving more weight to less frequent n-grams. |
| Abstract | Combining the two techniques, we show that using a fast shift-reduce parser we can achieve significant quality gains in NIST 2008 English-to-Chinese track (1.3 BLEU points over a phrase-based system, 0.8 BLEU points over a hierarchical phrase-based system). |
| Experiments | For English-to-Chinese translation, we used all the allowed training sets in the NIST 2008 constrained track. |
| Experiments | For NIST , we filtered out sentences exceeding 80 words in the parallel texts. |