Learn, hack!

URL

http://dewy.fem.tu-ilmenau.de/CCC/23C3/video/23C3-1701-en-open_source_machine_translation.m4v

File name

23C3-1701-en-open_source_machine_translation.m4v

File size

127.6 MB

MD5

63e10d6bd082cf61802321a10c0359cc

SHA1

2d439666644c574b8ba4206a20316929f49f24cb

Today two revolutions are pushing the machine translation field forward: the open source movement, and the broader application of statistical methods. This talk is at the intersection of the two: centering around the applications and contributions to be made to Moses, a state of the art open source toolkit for statistical machine translation developed by researchers from MIT, Edinburgh, Cornell, and Aachen. Today two revolutions are pushing the machine translation field forward: the open source movement, and the broader application of statistical methods. This talk is at the intersection of the two: centering around the applications and contributions to be made to Moses, a state of the art open source toolkit for statistical machine translation developed by researchers from MIT, Edinburgh, Cornell, and Aachen. In the past, those who wanted quality machine translations were forced to rely on closed source, rule based engines such a SYSTRAN. Even most of Google's translation engine uses SYSTRAN software. But Google and others are moving towards flexible, trainable systems, based on computer generated statistics rather than PhD linguist generated rules. This means a machine translation is accessible to the average user. Next time, instead of getting angry or amused by a poor translation provided by Google Translate or BabelFish, use your own copy of the open source engine and you can hack away, helping to improve translation quality for yourself and users around the globe.