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Approches en corpus pour la traduction Le cas MÉTÉO
| Content Provider | Semantic Scholar |
|---|---|
| Author | Langlais, Philippe Leplus, Thomas Gandrabur, Simona |
| Copyright Year | 2005 |
| Abstract | 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 r tionally reconstructedone 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 better customization possibilities. Langlais, Leplus, Gandrabur et Lapalme |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.iro.umontreal.ca/~felipe/Papers/slides-taln2005-meteo.pdf |
| Alternate Webpage(s) | http://rali.iro.umontreal.ca/rali/sites/default/files/publis/MeteoTALN05.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
