Learning finite-state models for machine translation
نویسندگان
چکیده
منابع مشابه
Machine Translation using Neural Networks and Finite-State Models*
Both Neural Networks and Finite-State Models have recently proved to be encouraging approaches to Example-Based Machine Translation. This paper compares the translation performances achieved with the two techniques as well as the corresponding resources required. To this end, both Elman Simple Recurrent Nets and Subsequential Transducers were trained to tackle a simple pseudo-natural machine tr...
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Current methodologies for automatic translation cannot be expected to produce high quality translations. However, some techniques based on these methodologies can increase the productivity of human translators. The basis of one of these methodologies are finite-state transducers, which are adequate models for computer assisted translation. These models have proved its efficiency in many pattern...
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State-of-the-art machine translation techniques are still far from producing high quality translations. This drawback leads us to introduce an alternative approach to the translation problem that brings human expertise into the machine translation scenario. In this framework, namely Computer Assisted Translation (CAT), human translators interact with a translation system, as an assistance tool,...
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Statistical techniques and grammatical inference have been used for dealing with automatic speech recognition with success, and can also be used for speech-to-speech machine translation. In this paper, new advances on a method for finite-state transducer inference are presented. This method has been tested experimentally in a speech-input translation task using a recognizer that allows a flexib...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 2006
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-006-9612-9