Abstract | Our models improve translation quality over the single generic label approach of Chiang (2005) and perform on par with the syntactically motivated approach from Zollmann and Venugopal (2006) on the N IST large Chinese—to—English translation task . |
Conclusion and discussion | Evaluated on a Chinese-to-English translation task , our approach improves translation quality over a popular PSCFG baseline—the hierarchical model of Chiang (2005) —and performs on par |
Experiments | We evaluate our approach by comparing translation quality, as evaluated by the IBM-BLEU (Papineni et al., 2002) metric on the NIST Chinese-to-English translation task using MT04 as development set to train the model parameters A, and MTOS, MT06 and MT08 as test sets. |
Introduction | Since the number of classes is a parameter of the clustering method and the resulting nonterminal size of our grammar is a function of the number of word classes, the PSCFG grammar complexity can be adjusted to the specific translation task at hand. |
Abstract | We show, using both automatic evaluation scores and linguistically motivated analyses of the output, that our methods outperform previously proposed ones and provide the best known results on the English-Finnish Europarl translation task . |
Conclusion and Future Work | Using our proposed approach we obtain better scores than the state of the art on the English-Finnish translation task (Luong et al., 2010): from 14.82% BLEU to 15.09%, while using a |
Translation and Morphology | Both of these approaches beat the state of the art on the English-Finnish translation task . |
Abstract | This allows for a completely probabilistic model that is able to create a phrase table that achieves competitive accuracy on phrase-based machine translation tasks directly from unaligned sentence pairs. |
Experimental Evaluation | We evaluate the proposed method on translation tasks from four languages, French, German, Spanish, and Japanese, into English. |
Experimental Evaluation | For Japanese, we use data from the NTCIR patent translation task (Fujii et al., 2008). |