Parsing with Lexicalized Probabilistic Recursive Transition Networks

Nasr, Alexis; Rambow, Owen

doi:10.1007/11780885_16

Cited by 1 publication

(1 citation statement)

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“…We parse all of the translated sentences in Ghmt, GhPhar, and Pharaoh in addition to all the references for all the studied test sets. We use the MICA dependency parser (Nasr and Rambow 2006). The translational SB precision is computed as follows: for each SB in the translated output, we consider a match to be present if the same SB appears in any of the references.…”

Section: Span-specific Syntactic Dependency Precision Evaluationmentioning

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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Section: Span-specific Syntactic Dependency Precision Evaluationmentioning

confidence: 99%