What is the difference between rule-based machine translation and statistical machine translation?
1. Rule-based MT systems require extensive dictionaries containing syntactic, semantic and morphological data, and large sets of rules to translate a word or phrase. Of multi-language, broad domain systems, Systran is the industry standard and benchmark for automatic translation, and relies on rule-based technology developed by a large team of linguists over many years. Other best-of-breed rule-based engines exist for specific language pairs, but their quality in a particular language pair does not necessarily scale to other pairs.2. Statistical MT is based on machine-learning technologies, and relies on large volumes of parallel human-translated texts from which the MT engine can learn. This data must be obtained in every language pair and domain that the machine will be asked to translate in. While the quality of current systems is limited by the availability of parallel data, the potential for language coverage and quality improvements is very promising, as multilingual content on t
1. Rule-based MT systems require extensive dictionaries containing syntactic, semantic and morphological data, and large sets of rules to translate a word or phrase. Of multi-language, broad domain systems, Systran is the industry standard and benchmark for automatic translation, and relies on rule-based technology developed by a large team of linguists over many years. Other best-of-breed rule-based engines exist for specific language pairs, but their quality in a particular language pair does not necessarily scale to other pairs. 2. Statistical MT is based on machine-learning technologies, and relies on large volumes of parallel human-translated texts from which the MT engine can learn. This data must be obtained in every language pair and domain that the machine will be asked to translate in. While the quality of current systems is limited by the availability of parallel data, the potential for language coverage and quality improvements is very promising, as multilingual content on
