Machine translation system and machine translation method based on syntactic analysis and hierarchical model

Abstract

The invention discloses a machine translation system and a machine translation method based on a syntactic analysis and hierarchical model. The machine translation system comprises a word alignment module, a phrase extraction module, a part-of-speech and syntax tagging module, a syntax-based non-contiguous phrase extraction module, a non-contiguous-phrase-based translation module, and a grading output module. In the machine translation system and the machine translation method, syntactic analysis is carried out on the basis of a general contiguous-phrase-based machine translation model, so that a syntax-based phrase rule base is extracted from a bilingual sentence alignment text, the problem of non-continuous fixed collocation of the context of the whole sentence is solved, and the invention accords with the syntactic characteristics of a language. Translation is carried out based on a non-contiguous phrase rule base and a phrase alignment table, and a translation result is graded based on an assessment model, so a translation effect is effectively improved.

Description

technical field [0001] The present invention relates to machine translation, in particular to a machine translation system and method based on syntax analysis and hierarchical model. Background technique [0002] Machine translation is an automatic translation of one natural language into another natural language. There are many types of machine translation systems, and the current popular one is the continuous phrase-based machine translation (PBMT) system. The problem to be solved by machine translation is to use computers to automatically translate sentences or fragments in the source language (SL) into corresponding sentences or fragments in the target language (TL). Corpus-based machine translation includes a bilingual alignment corpus (that is, each source language sentence has one or more translations corresponding to the target language), and the data and knowledge required for automatic computer translation are obtained from the corpus. [0003] The PBMT system use...

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Problems solved by technology

[0004] PBMT has the following defects: (1) due to local context dependencies, PBMT cannot handle long sentences or phrases well, especially the long-distance ordering problem caused by non-continuous fixed collocations; (2) because PBMT is completely Relying on continuous phrase statistics, ignoring the syntactic features of the language, failing to make full use of the knowledge contained in the corpus, thus limiting the further improvement of its translation effect

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