Syntax Augmented Inversion Transduction Grammars for Machine Translation
| Autor: | Joan Andreu Sánchez Peiró, Guillem Gascó I Mora |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Parsing
Syntax (programming languages) Machine translation business.industry Computer science Speech recognition InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Parse tree Transfer-based machine translation computer.software_genre Syntax Inversion transduction grammars Example-based machine translation ComputingMethodologies_PATTERNRECOGNITION Rule-based machine translation Synchronous context-free grammar Artificial intelligence Computational linguistics business computer Natural language processing BLEU |
| Zdroj: | Computational Linguistics and Intelligent Text Processing ISBN: 9783642121159 CICLing |
| DOI: | 10.1007/978-3-642-12116-6_36 |
| Popis: | In this paper we propose a novel method for inferring an Inversion Transduction Grammar (ITG) from a bilingual parallel corpus with linguistic information from the source or target language. Our method combines bilingual ITG parse trees with monolingual linguistic trees in order to obtain a Syntax Augmented ITG (SAITG). The use of a modified bilingual parsing algorithm with bracketing information makes possible that each bilingual subtree has a correspondent subtree in the monolingual parsing. In addition, several binarization techniques have been tested for the resulting SAITG. In order to evaluate the effects of the use of SAITGs in Machine Translation tasks, we have used them in an ITG-based machine translation decoder. The results obtained using SAITGs with the decoder for the IWSLT-08 Chinese-English machine translation task produce significant improvements in BLEU. |
| Databáze: | OpenAIRE |
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