ArchTran: A Corpus-based Statistics-oriented English-Chinese Machine Translation System

611

Authors

nan

Abstract

AbstractThe ArchTran English-Chinese Machine Translation System is among the first commercialized English-Chinese machine translation systems in the world. A prototype system was released in 1989 and currently serves as the kernel of a value-added network-based translation service. The main design features of the ArchTran system are the adoption of a mixed (bottom-up parsing with top-down filtering) parsing strategy, a scored parsing mechanism, and the corpus-based, statistics-oriented paradigm for linguistic knowledge acquisition. Under this framework, research directions are toward designing systematic and automatic methods for acquiring language model parameters, and toward using preference measure with uniform probabilistic score function for ambiguity resolution. In this paper, the underlying probabilistic models of the ArchTran designing philosophy will be presented.

Keywords

nan

Full Content

[MT Summit III, Washington, DC, USA, July 1991]

Labeled Text

AbstractThe ArchTran English-Chinese Machine Translation System is among the first commercialized English-Chinese machine translation systems in the world. A prototype system was released in 1989 and currently serves as the kernel of a value-added network-based translation service. The main design features of the ArchTran system are the adoption of a mixed (bottom-up parsing with top-down filtering) parsing strategy, a scored parsing mechanism, and the corpus-based, statistics-oriented paradigm for linguistic knowledge acquisition. Under this framework, research directions are toward designing systematic and automatic methods for acquiring language model parameters, and toward using preference measure with uniform probabilistic score function for ambiguity resolution. In this paper, the underlying probabilistic models of the ArchTran designing philosophy will be presented.

Publication Details

Paper ID:
611
Year:
1991
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan