What's New in Statistical Machine Translation

Kevin Knight, Philipp Koehn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Automatic translation from one human language to another using computers, better known as machine translation (MT), is a long-standing goal of computer science. Accurate translation requires a great deal of knowledge about the usage and meaning of words, the structure of phrases, the meaning of sentences, and which real-life situations are plausible. For general-purpose translation, the amount of required knowledge is staggering, and it is not clear how to prioritize knowledge acquisition efforts.Recently, there has been a fair amount of research into extracting translation-relevant knowledge automatically from bilingual texts. In the early 1990s, IBM pioneered automatic bilingual-text analysis. A 1999 workshop at Johns Hopkins University saw a re-implementation of many of the core components of this work, aimed at attracting more researchers into the field. Over the past years, several statistical MT projects have appeared in North America, Europe, and Asia, and the literature is growing substantially. We will provide a technical overview of the state-of-the-art.
Original languageEnglish
Title of host publicationProceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Tutorials - Volume 5
Place of PublicationStroudsburg, PA, USA
PublisherAssociation for Computational Linguistics
Pages5-5
Number of pages1
DOIs
Publication statusPublished - 2003

Publication series

NameNAACL-Tutorials '03
PublisherAssociation for Computational Linguistics

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