Thomas Leplus

Machine Translation (MT) is the focus of extensive scientific investigations driven by regular evaluation campaigns, but which are mostly oriented towards a somewhat artificial task: translating news articles into English. In this paper, we investigate how well current MT approaches deal with a real-world task. We have rationally reconstructed one of the only MT systems in daily production use: the METEO system. We show how a combination of a sentence-based memory approach, a phrase-based… Show more

Machine Translation (MT) is the focus of extensive scientific investigations driven by regular evaluation campaigns, but which are mostly oriented towards a somewhat artificial task: translating news articles into English. In this paper, we investigate how well current MT approaches deal with a real-world task. We have rationally reconstructed one of the only MT systems in daily production use: the METEO system. We show how a combination of a sentence-based memory approach, a phrase-based statistical engine and a neural-network rescorer can give results comparable to those of the current system while offering a faster development cycle and arguably better customization possibilities. Show less

Machine Translation (MT) is the focus of extensive scientific investigations driven by regular evaluation campaigns, but which are mostly oriented towards a somewhat particular task: translating news articles into English. In this paper, we investigate how well current MT approaches deal with a real-world task. We have rationally reconstructed one of the only MT systems in daily use which produces high-quality translation: the Météo system. We show how a combination of a sentence-based memory… Show more

Machine Translation (MT) is the focus of extensive scientific investigations driven by regular evaluation campaigns, but which are mostly oriented towards a somewhat particular task: translating news articles into English. In this paper, we investigate how well current MT approaches deal with a real-world task. We have rationally reconstructed one of the only MT systems in daily use which produces high-quality translation: the Météo system. We show how a combination of a sentence-based memory approach, a phrase-based statistical engine and a neural-network rescorer can give results comparable to those of the current system. We also explore another possible prospect for MT technology: the translation of weather alerts, which are currently being translated manually by translators at the Canadian Translation Bureau. Show less

Weather Report Translation using a Translation Memory.
Thomas Leplus, Philippe Langlais and Guy Lapalme.
Association for Machine Translation in the Americas (AMTA-2004)
Georgetown University, Washington DC (USA), September