The field of machine translation has recently been energized by the emergence
of statistical techniques, which have brought the dream of automatic
language translation closer to reality. This class-tested textbook, authored
by an active researcher in the field, provides a gentle and accessible introduction
to the latest methods and enables the reader to build machine
translation systems for any language pair.
It provides the necessary grounding in linguistics and probabilities,
and covers the major models for machine translation: word-based, phrasebased,
and tree-based, as well as machine translation evaluation, language
modeling, discriminative training and advanced methods to integrate linguistic
annotation. The book reports on the latest research and outstanding
challenges, and enables novices as well as experienced researchers to make
contributions to the field. It is ideal for students at undergraduate and graduate
level, or for any reader interested in the latest developments in machine
translation.
P H I L I P P KOEHN is a lecturer in the School of Informatics at the University
of Edinburgh. He is the scientific coordinator of the European
EuroMatrix project and is also involved in research funded by DARPA in
the USA. He has also collaborated with leading companies in the field,
such as Systran and Asia Online. He implemented the widely used decoder
Pharaoh, and is leading the development of the open source machine
translation toolkit Moses.
2019-12-21 21:02:03
5.45MB
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机翻后手动修改，排版按照原论文格式，格式上有些地方有点问题。
2019-12-21 20:54:45
206KB
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