DUSTer: A Method for Unraveling Cross-Language Divergences for Statistical Word-Level Alignment
AMTA '02 Proceedings of the 5th Conference of the Association for Machine Translation in the Americas on Machine Translation: From Research to Real Users
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
A comparison of alignment models for statistical machine translation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Inducing multilingual text analysis tools via robust projection across aligned corpora
HLT '01 Proceedings of the first international conference on Human language technology research
A cheap and fast way to build useful translation lexicons
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A syntax-based statistical translation model
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
An evaluation exercise for word alignment
HLT-NAACL-PARALLEL '03 Proceedings of the HLT-NAACL 2003 Workshop on Building and using parallel texts: data driven machine translation and beyond - Volume 3
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Experiments in morphosyntactic processing for translating to and from German
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
Diversify and combine: improving word alignment for machine translation on low-resource languages
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
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We present an improved method for automated word alignment of parallel texts which takes advantage of knowledge of syntactic divergences, while avoiding the need for syntactic analysis of the less resource rich language, and retaining the robustness of syntactically agnostic approaches such as the IBM word alignment models. We achieve this by using simple, easily-elicited knowledge to produce syntax-based heuristics which transform the target language (e.g. English) into a form more closely resembling the source language, and then by using standard alignment methods to align the transformed bitext. We present experimental results under variable resource conditions. The method improves word alignment performance for language pairs such as English-Korean and English-Hindi, which exhibit longer-distance syntactic divergences.