@inproceedings{watanabe-etal-2007-larger,
title = "Larger feature set approach for machine translation in {IWSLT} 2007",
author = "Watanabe, Taro and
Suzuki, Jun and
Sudoh, Katsuhito and
Tsukada, Hajime and
Isozaki, Hideki",
booktitle = "Proceedings of the Fourth International Workshop on Spoken Language Translation",
month = oct # " 15-16",
year = "2007",
address = "Trento, Italy",
url = "https://aclanthology.org/2007.iwslt-1.16/",
abstract = "The NTT Statistical Machine Translation System employs a large number of feature functions. First, k-best translation candidates are generated by an efficient decoding method of hierarchical phrase-based translation. Second, the k-best translations are reranked. In both steps, sparse binary features {---} of the order of millions {---} are integrated during the search. This paper gives the details of the two steps and shows the results for the Evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2007."
}
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<abstract>The NTT Statistical Machine Translation System employs a large number of feature functions. First, k-best translation candidates are generated by an efficient decoding method of hierarchical phrase-based translation. Second, the k-best translations are reranked. In both steps, sparse binary features — of the order of millions — are integrated during the search. This paper gives the details of the two steps and shows the results for the Evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2007.</abstract>
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%0 Conference Proceedings
%T Larger feature set approach for machine translation in IWSLT 2007
%A Watanabe, Taro
%A Suzuki, Jun
%A Sudoh, Katsuhito
%A Tsukada, Hajime
%A Isozaki, Hideki
%S Proceedings of the Fourth International Workshop on Spoken Language Translation
%D 2007
%8 oct 15 16
%C Trento, Italy
%F watanabe-etal-2007-larger
%X The NTT Statistical Machine Translation System employs a large number of feature functions. First, k-best translation candidates are generated by an efficient decoding method of hierarchical phrase-based translation. Second, the k-best translations are reranked. In both steps, sparse binary features — of the order of millions — are integrated during the search. This paper gives the details of the two steps and shows the results for the Evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2007.
%U https://aclanthology.org/2007.iwslt-1.16/
Markdown (Informal)
[Larger feature set approach for machine translation in IWSLT 2007](https://aclanthology.org/2007.iwslt-1.16/) (Watanabe et al., IWSLT 2007)
ACL