Statistical machine translation by parsing

Authors:
I. Dan Melamed
Affiliations:
New York University, New York, NY
Venue:
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Year:
2004

Citing 18
Cited 37

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Abstract

In an ordinary syntactic parser, the input is a string, and the grammar ranges over strings. This paper explores generalizations of ordinary parsing algorithms that allow the input to consist of string tuples and/or the grammar to range over string tuples. Such algorithms can infer the synchronous structures hidden in parallel texts. It turns out that these generalized parsers can do most of the work required to train and apply a syntax-aware statistical machine translation system.