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    <abstract>This paper describes NICT’s participation in the IWSLT 2010 evaluation campaign for the DIALOG translation (Chinese-English) and the BTEC (French-English) translation shared-tasks. For the DIALOG translation, the main challenge to this task is applying context information during translation. Context information can be used to decide on word choice and also to replace missing information during translation. We applied discriminative reranking using contextual information as additional features. In order to provide more choices for re-ranking, we generated n-best lists from multiple phrase-based statistical machine translation systems that varied in the type of Chinese word segmentation schemes used. We also built a model that merged the phrase tables generated by the different segmentation schemes. Furthermore, we used a lattice-based system combination model to combine the output from different systems. A combination of all of these systems was used to produce the n-best lists for re-ranking. For the BTEC task, a general approach that used latticebased system combination of two systems, a standard phrasebased system and a hierarchical phrase-based system, was taken. We also tried to process some unknown words by replacing them with the same words but different inflections that are known to the system.</abstract>
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%0 Conference Proceedings
%T The NICT translation system for IWSLT 2010
%A Goh, Chooi-Ling
%A Watanabe, Taro
%A Paul, Michael
%A Finch, Andrew
%A Sumita, Eiichiro
%S Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign
%D 2010
%8 dec 2 3
%C Paris, France
%F goh-etal-2010-nict
%X This paper describes NICT’s participation in the IWSLT 2010 evaluation campaign for the DIALOG translation (Chinese-English) and the BTEC (French-English) translation shared-tasks. For the DIALOG translation, the main challenge to this task is applying context information during translation. Context information can be used to decide on word choice and also to replace missing information during translation. We applied discriminative reranking using contextual information as additional features. In order to provide more choices for re-ranking, we generated n-best lists from multiple phrase-based statistical machine translation systems that varied in the type of Chinese word segmentation schemes used. We also built a model that merged the phrase tables generated by the different segmentation schemes. Furthermore, we used a lattice-based system combination model to combine the output from different systems. A combination of all of these systems was used to produce the n-best lists for re-ranking. For the BTEC task, a general approach that used latticebased system combination of two systems, a standard phrasebased system and a hierarchical phrase-based system, was taken. We also tried to process some unknown words by replacing them with the same words but different inflections that are known to the system.
%U https://aclanthology.org/2010.iwslt-evaluation.18
%P 139-146

Markdown (Informal)

[The NICT translation system for IWSLT 2010](https://aclanthology.org/2010.iwslt-evaluation.18) (Goh et al., IWSLT 2010)

• The NICT translation system for IWSLT 2010 (Goh et al., IWSLT 2010)

ACL

• Chooi-Ling Goh, Taro Watanabe, Michael Paul, Andrew Finch, and Eiichiro Sumita. 2010. The NICT translation system for IWSLT 2010. In Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign, pages 139–146, Paris, France.