Rule-based machine translation
Encyclopedia
Rule-based Machine Translation (RBMT; also known as “Knowledge-based Machine Translation”; “Classical Approach” of MT) is a general term that denotes machine translation systems based on linguistic information about source and target languages basically retrieved from (bilingual) dictionaries and grammars covering the main semantic, morphological, and syntactic regularities of each language respectively. Having input sentences (in some source language), an RBMT system generates them to output sentences (in some target language) on the basis of morphological, syntactic, and semantic analysis of both the source and the target languages involved in a concrete translation task.
Two different types of rule-based machine translation systems can be differentiated from each other: Transfer RBMT Systems (Transfer Based Machine Translation
) and Interlingual RBMT Systems (Interlingua
). RBMT systems can also be characterized as the systems opposite to Example-based Systems of Machine Translation (Example-based machine translation
), whereas Hybrid Machine Translations Systems make use of many principles derived from RBMT.
History
The first RBMT systems were developed in the early 1970s and underwent their evolution till 1990s. The most important steps of this evolution were the emergence of the following RBMT systems:
- Systran (http://www.systran.de/)
- Japanese MT systems (http://www.wtec.org/loyola/ar93_94/mt.htm)
- EUROTRA (Eurotra
)
Basic principles
The main approach of RBMT systems is based on linking the structure of the given input sentence with the structure of the demanded output sentence, necessarily preserving their unique meaning. The following example can illustrate the general frame of RBMT:
A girl eats an apple. (Source Language = English); Demanded Target Language = German
Minimally, to get a German translation of this English sentence one needs:
1) A dictionary that will map each English word to an appropriate German word.
2) Rules representing regular English sentence structure.
3) Rules representing regular German sentence structure.
And finally, we need rules according to which one can relate these two structures together.
Accordingly we can state the following stages of translation:
1st: getting basic part-of-speech information of each source word:
a = indef.article; girl = noun; eats = verb; an = indef.article; apple = noun
2nd: getting syntactic information about the verb “to eat”:
NP-eat-NP; here: eat – Present Simple, 3rd Person Singular, Active Voice
3rd: parsing the source sentence:
(NP einen Apfel) = the object of eat
Often only partial parsing is sufficient to get to the syntactic structure of the source sentence and to map it onto the structure of the target sentence.
4th: translate English words into German
a (category = indef.article) => ein (category = indef.article)
girl (category = noun) => Mädchen (category = noun)
eat (category = verb) => essen (category = verb)
an (category = indef. article) => ein (category = indef.article)
apple (category = noun) => Apfel (category = noun)
5th: Mapping dictionary entries into appropriate inflected forms (final generation):
A girl eats an apple. => Ein Mädchen isst einen Apfel.
Reasons of Suppression
- Insufficient amount of really good dictionaries. Building new dictionaries is expensive.
- Some linguistic information still needs to be set manually.
- It is hard to deal with rule interactions in big systems, ambiguity, and idiomatic expressions.
- Limited capacity of computers at that time.
Two different types of rule-based machine translation systems can be differentiated from each other: Transfer RBMT Systems (Transfer Based Machine Translation
Transfer-based machine translation
Transfer-based machine translation is a type of machine translation. It is based on the idea of interlingua and is currently one of the most widely used methods of machine translation-Overview:...
) and Interlingual RBMT Systems (Interlingua
Interlingua
Interlingua is an international auxiliary language , developed between 1937 and 1951 by the International Auxiliary Language Association...
). RBMT systems can also be characterized as the systems opposite to Example-based Systems of Machine Translation (Example-based machine translation
Example-based machine translation
The example-based machine translation approach to machine translation is often characterized by its use of a bilingual corpus with parallel texts as its main knowledge base, at run-time...
), whereas Hybrid Machine Translations Systems make use of many principles derived from RBMT.
History
The first RBMT systems were developed in the early 1970s and underwent their evolution till 1990s. The most important steps of this evolution were the emergence of the following RBMT systems:
- Systran (http://www.systran.de/)
- Japanese MT systems (http://www.wtec.org/loyola/ar93_94/mt.htm)
- EUROTRA (Eurotra
Eurotra
Eurotra was an ambitious machine translation project established and funded by the European Commission from the late 1970s until 1994.Emboldened by modest success with an older, commercially-developed machine translation system SYSTRAN, a large network of European computational linguists embarked...
)
Basic principles
The main approach of RBMT systems is based on linking the structure of the given input sentence with the structure of the demanded output sentence, necessarily preserving their unique meaning. The following example can illustrate the general frame of RBMT:
A girl eats an apple. (Source Language = English); Demanded Target Language = German
Minimally, to get a German translation of this English sentence one needs:
1) A dictionary that will map each English word to an appropriate German word.
2) Rules representing regular English sentence structure.
3) Rules representing regular German sentence structure.
And finally, we need rules according to which one can relate these two structures together.
Accordingly we can state the following stages of translation:
1st: getting basic part-of-speech information of each source word:
a = indef.article; girl = noun; eats = verb; an = indef.article; apple = noun
2nd: getting syntactic information about the verb “to eat”:
NP-eat-NP; here: eat – Present Simple, 3rd Person Singular, Active Voice
3rd: parsing the source sentence:
(NP einen Apfel) = the object of eat
Often only partial parsing is sufficient to get to the syntactic structure of the source sentence and to map it onto the structure of the target sentence.
4th: translate English words into German
a (category = indef.article) => ein (category = indef.article)
girl (category = noun) => Mädchen (category = noun)
eat (category = verb) => essen (category = verb)
an (category = indef. article) => ein (category = indef.article)
apple (category = noun) => Apfel (category = noun)
5th: Mapping dictionary entries into appropriate inflected forms (final generation):
A girl eats an apple. => Ein Mädchen isst einen Apfel.
Reasons of Suppression
- Insufficient amount of really good dictionaries. Building new dictionaries is expensive.
- Some linguistic information still needs to be set manually.
- It is hard to deal with rule interactions in big systems, ambiguity, and idiomatic expressions.
- Limited capacity of computers at that time.