Inducing lexico-structural transfer rules from parsed Bi-texts

Lavoie, Benoit; White, Michael; Korelsky, Tanya

doi:10.3115/1118037.1118040

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…The transfer rule induction process has the following steps described below (additional details can also be found in (Lavoie et. al., 2001)):…”

Section: Transfer Rule Inductionmentioning

confidence: 99%

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Learning domain-specific transfer rules

Lavoie¹,

White²,

Korelsky³

2002

COLING-02 on Machine Translation in Asia -

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We describe the design of an MT system that employs transfer rules induced from parsed bitexts and present evaluation results. The system learns lexico-structural transfer rules using syntactic pattern matching, statistical co-occurrence and error-driven filtering. In an experiment with domain-specific Korean to English translation, the approach yielded substantial improvements over three base-line systems.

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“…The transfer rule induction process has the following steps described below (additional details can also be found in (Lavoie et. al., 2001)):…”

Section: Transfer Rule Inductionmentioning

confidence: 99%

“…), and derives rules con-sisting of any source and target tree sub-patterns matching a subset of the parse features. A more detailed description of the differences can be found in (Lavoie et. al., 2001).…”

Section: Introductionmentioning

confidence: 99%

Learning domain-specific transfer rules

Lavoie¹,

White²,

Korelsky³

2002

COLING-02 on Machine Translation in Asia -

Self Cite

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“…7 A pure transfer approach is a brute force attempt to manually encode all translation divergences in a transfer lexicon (Dorr et al 1999). Very large parsed and word-aligned bilingual corpora have also been used to automatically extract transfer rules (Watanabe et al 2000;Lavoie et al 2001).…”

Section: Handling Translation Divergencesmentioning

confidence: 99%

Symbolic-to-statistical hybridization: extending generation-heavy machine translation

Habash

Dorr

Monz

2009

Machine Translation

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The last few years have witnessed an increasing interest in hybridizing surface-based statistical approaches and rule-based symbolic approaches to machine translation (MT). Much of that work is focused on extending statistical MT systems with symbolic knowledge and components. In the brand of hybridization discussed here, we go in the opposite direction: adding statistical bilingual components to a symbolic system. Our base system is Generation-heavy machine translation (GHMT), a primarily symbolic asymmetrical approach that addresses the issue of Interlingual MT resource poverty in source-poor/target-rich language pairs by exploiting symbolic and statistical target-language resources. GHMT's statistical components are limited to target-language models, which arguably makes it a simple form of a hybrid system. We extend the hybrid nature of GHMT by adding statistical bilingual components. We also describe the details of retargeting it to Arabic-English MT. The morphological richness of Arabic brings several challenges to the hybridization task. We conduct an extensive evaluation of multiple system variants. Our evaluation shows that this new variant of GHMT-a primarily symbolic system extended with monolingual and bilingual statistical components-has a higher degree of grammaticality than a phrase-based statistical MT system, where grammaticality is measured in terms of correct verb-argument realization and long-distance dependency translation.

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Better Contextual Translation Using Machine Learning

Menezes

2002

Machine Translation: From Research to Real Users

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Inducing lexico-structural transfer rules from parsed Bi-texts

Abstract: This paper describes a novel approach to inducing lexico-structural transfer rules from parsed bi-texts using syntactic pattern matching, statistical cooccurrence and error-driven filtering. We present initial evaluation results and discuss future directions.

Cited by 11 publications

References 10 publications

Learning domain-specific transfer rules

Learning domain-specific transfer rules

Symbolic-to-statistical hybridization: extending generation-heavy machine translation

Better Contextual Translation Using Machine Learning

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