Machine Translation
ABSTRACT
Most research in Machine translation is about having the computers completely bear the load of translating one human language into another. This paper looks at the machine translation problem afresh and observes that there is a need to share the load between man and machine, distinguish ‘reliable’ knowledge from the ‘heuristics’, provide a spectrum of outputs to serve different strata of people, and finally make use of existing resources instead of reinventing the wheel. This paper describes the architecture and design based on the fundamental premise of sharing the load, resulting in “good enough” results according to the needs of the reader. The architecture differs from the conventional in three major ways: 1. Reversal in the order of operations as compared to conventional machine translation systems 2. Introduction of interfaces that act like glue and improve the modularity of the system 3. Development of a GUI to provide the ‘right ‘ amount of information at the right time The paper attempts to prove that this new architecture is a better approach to Machine translation process transparent to the user-cum-developer; and it leads to machine translation in stages, thus ensuring robustness.
INTRODUCTION
Machine translation, sometimes referred to by the abbreviation MT, is a sub-field of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to another. At its basic level, MT performs simple substitution of words in one natural language for words in another. Using corpus techniques, more complex translations may be attempted, allowing for better handling of differences in linguistic typology, phrase recognition, and translation of idioms, as well as the isolation of anomalies.
Current machine translation software often allows for customization by domain or profession (such as weather reports) — improving output by limiting the scope of allowable substitutions.
Most research in Machine translation is about having the computers completely bear the load of translating one human language into another. This paper looks at the machine translation problem afresh and observes that there is a need to share the load between man and machine, distinguish ‘reliable’ knowledge from the ‘heuristics’, provide a spectrum of outputs to serve different strata of people, and finally make use of existing resources instead of reinventing the wheel. This paper describes the architecture and design based on the fundamental premise of sharing the load, resulting in “good enough” results according to the needs of the reader. The architecture differs from the conventional in three major ways: 1. Reversal in the order of operations as compared to conventional machine translation systems 2. Introduction of interfaces that act like glue and improve the modularity of the system 3. Development of a GUI to provide the ‘right ‘ amount of information at the right time The paper attempts to prove that this new architecture is a better approach to Machine translation process transparent to the user-cum-developer; and it leads to machine translation in stages, thus ensuring robustness.
INTRODUCTION
Machine translation, sometimes referred to by the abbreviation MT, is a sub-field of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to another. At its basic level, MT performs simple substitution of words in one natural language for words in another. Using corpus techniques, more complex translations may be attempted, allowing for better handling of differences in linguistic typology, phrase recognition, and translation of idioms, as well as the isolation of anomalies.
Current machine translation software often allows for customization by domain or profession (such as weather reports) — improving output by limiting the scope of allowable substitutions.