Statistical transfer systems for French--English and German--English machine translation

Authors:
Greg Hanneman;Edmund Huber;Abhaya Agarwal;Vamshi Ambati;Alok Parlikar;Erik Peterson;Alon Lavie
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA
Venue:
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Year:
2008

Citing 6
Cited 3

A systematic comparison of various statistical alignment models

Computational Linguistics
BLEU: a method for automatic evaluation of machine translation

ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Syntax-driven learning of sub-sentential translation equivalents and translation rules from parsed parallel corpora

SSST '08 Proceedings of the Second Workshop on Syntax and Structure in Statistical Translation
(Meta-) evaluation of machine translation

StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Meteor: an automatic metric for MT evaluation with high levels of correlation with human judgments

StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Stat-XFER: a general search-based syntax-driven framework for machine translation

CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing

Further meta-evaluation of machine translation

StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
An improved statistical transfer system for French--English machine translation

StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
Panning for EBMT gold, or "Remembering not to forget"

Machine Translation

Quantified Score

Hi-index	0.00

Visualization

Abstract

We apply the Stat-XFER statistical transfer machine translation framework to the task of translating from French and German into English. We introduce statistical methods within our framework that allow for the principled extraction of syntax-based transfer rules from parallel corpora given word alignments and constituency parses. Performance is evaluated on test sets from the 2007 WMT shared task.