Index of papers in Proc. ACL 2011 that mention
  • translation task
Zollmann, Andreas and Vogel, Stephan
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.
translation task is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Clifton, Ann and Sarkar, Anoop
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 .
translation task is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Neubig, Graham and Watanabe, Taro and Sumita, Eiichiro and Mori, Shinsuke and Kawahara, Tatsuya
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).
translation task is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: