This study evaluates the impact of integrating two different collocation segmentations methods in a standard phrase-based statistical machine translation approach. The collocation segmentation techniques are implemented simultaneously in the source and target side. Each resulting collocation segmentation is used to extract translation units. Experiments are reported in the English-to-Spanish Bible task and promising results (an improvement over 0.7 BLEU absolute) are achieved in translation quality.Postprint (published version
Collocations are notoriously difficult for non-native speakers to translate, primarily because they ...
The phrase table is considered to be the main bilingual resource for the phrase-based statistical ma...
The statistical framework has proved to be very successful in machine translation. The main reason ...
This study evaluates the impact of integrating two different collocation segmentations methods in a ...
This paper describes the 2010 phrase-based statistical machine translation system developed at the T...
This report evaluates the impact of using a novel collocation segmentation method for phrase extract...
Este artículo evalúa un nuevo método de segmentación en un sistema de traducción automática estadíst...
This paper describes the UPC-BMIC-VMU participation in the IWSLT 2010 evaluation campaign. The SMT s...
This paper proposes to use monolingual collocations to improve Statistical Ma-chine Translation (SMT...
International audienceIn this paper, we introduce two new methods dedicated to phrase based machine ...
We introduce a word segmentation approach to languages where word boundaries are not orthographicall...
We introduce a word segmentation ap-proach to languages where word bound-aries are not orthographica...
We present a method for improving statistical machine translation performance by using linguisticall...
In this paper, we examine a number of different phrase segmentation approaches for Machine Translati...
Statistical Machine Translation (SMT) developed in the late 1980s, based initially upon a word-to-wo...
Collocations are notoriously difficult for non-native speakers to translate, primarily because they ...
The phrase table is considered to be the main bilingual resource for the phrase-based statistical ma...
The statistical framework has proved to be very successful in machine translation. The main reason ...
This study evaluates the impact of integrating two different collocation segmentations methods in a ...
This paper describes the 2010 phrase-based statistical machine translation system developed at the T...
This report evaluates the impact of using a novel collocation segmentation method for phrase extract...
Este artículo evalúa un nuevo método de segmentación en un sistema de traducción automática estadíst...
This paper describes the UPC-BMIC-VMU participation in the IWSLT 2010 evaluation campaign. The SMT s...
This paper proposes to use monolingual collocations to improve Statistical Ma-chine Translation (SMT...
International audienceIn this paper, we introduce two new methods dedicated to phrase based machine ...
We introduce a word segmentation approach to languages where word boundaries are not orthographicall...
We introduce a word segmentation ap-proach to languages where word bound-aries are not orthographica...
We present a method for improving statistical machine translation performance by using linguisticall...
In this paper, we examine a number of different phrase segmentation approaches for Machine Translati...
Statistical Machine Translation (SMT) developed in the late 1980s, based initially upon a word-to-wo...
Collocations are notoriously difficult for non-native speakers to translate, primarily because they ...
The phrase table is considered to be the main bilingual resource for the phrase-based statistical ma...
The statistical framework has proved to be very successful in machine translation. The main reason ...