Rule-based machine translation

Last updated January 18, 2025

Rule-based machine translation (RBMT; "Classical Approach" of MT) is machine translation systems based on linguistic information about source and target languages basically retrieved from (unilingual, bilingual or multilingual) 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. RBMT has been progressively superseded by more efficient methods, particularly neural machine translation.^[1]

History

The first RBMT systems were developed in the early 1970s. The most important steps of this evolution were the emergence of the following RBMT systems:

Systran
Japanese MT systems^[2]^[3]

Today, other common RBMT systems include:

Types of RBMT

There are three different types of rule-based machine translation systems:

Direct Systems (Dictionary Based Machine Translation) map input to output with basic rules.
Transfer RBMT Systems (Transfer Based Machine Translation) employ morphological and syntactical analysis.
Interlingual RBMT Systems (Interlingua) use an abstract meaning.^[4]^[5]

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.

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:

A dictionary that will map each English word to an appropriate German word.
Rules representing regular English sentence structure.
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 an apple) = 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.

Ontologies

An ontology is a formal representation of knowledge that includes the concepts (such as objects, processes etc.) in a domain and some relations between them. If the stored information is of linguistic nature, one can speak of a lexicon.^[6] In NLP, ontologies can be used as a source of knowledge for machine translation systems. With access to a large knowledge base, rule-based systems can be enabled to resolve many (especially lexical) ambiguities on their own. In the following classic examples, as humans, we are able to interpret the prepositional phrase according to the context because we use our world knowledge, stored in our lexicons:

I saw a man/star/molecule with a microscope/telescope/binoculars.^[6]

Since the syntax does not change, a traditional rule-based machine translation system may not be able to differentiate between the meanings. With a large enough ontology as a source of knowledge however, the possible interpretations of ambiguous words in a specific context can be reduced.^[6]

Building ontologies

The ontology generated for the PANGLOSS knowledge-based machine translation system in 1993 may serve as an example of how an ontology for NLP purposes can be compiled:^[7]^[8]

A large-scale ontology is necessary to help parsing in the active modules of the machine translation system.
In the PANGLOSS example, about 50,000 nodes were intended to be subsumed under the smaller, manually-built upper (abstract) region of the ontology. Because of its size, it had to be created automatically.
The goal was to merge the two resources LDOCE online and WordNet to combine the benefits of both: concise definitions from Longman, and semantic relations allowing for semi-automatic taxonomization to the ontology from WordNet.
- A definition match algorithm was created to automatically merge the correct meanings of ambiguous words between the two online resources, based on the words that the definitions of those meanings have in common in LDOCE and WordNet. Using a similarity matrix, the algorithm delivered matches between meanings including a confidence factor. This algorithm alone, however, did not match all meanings correctly on its own.
- A second hierarchy match algorithm was therefore created which uses the taxonomic hierarchies found in WordNet (deep hierarchies) and partially in LDOCE (flat hierarchies). This works by first matching unambiguous meanings, then limiting the search space to only the respective ancestors and descendants of those matched meanings. Thus, the algorithm matched locally unambiguous meanings (for instance, while the word seal as such is ambiguous, there is only one meaning of seal in the animal subhierarchy).
Both algorithms complemented each other and helped constructing a large-scale ontology for the machine translation system. The WordNet hierarchies, coupled with the matching definitions of LDOCE, were subordinated to the ontology's upper region. As a result, the PANGLOSS MT system was able to make use of this knowledge base, mainly in its generation element.

Components

The RBMT system contains:

a SL morphological analyser - analyses a source language word and provides the morphological information;
a SL parser - is a syntax analyser which analyses source language sentences;
a translator - used to translate a source language word into the target language;
a TL morphological generator - works as a generator of appropriate target language words for the given grammatica information;
a TL parser - works as a composer of suitable target language sentences;
Several dictionaries - more specifically a minimum of three dictionaries:

a SL dictionary - needed by the source language morphological analyser for morphological analysis,

a bilingual dictionary - used by the translator to translate source language words into target language words,

a TL dictionary - needed by the target language morphological generator to generate target language words.^[9]

The RBMT system makes use of the following:

a Source Grammar for the input language which builds syntactic constructions from input sentences;
a Source Lexicon which captures all of the allowable vocabulary in the domain;
Source Mapping Rules which indicate how syntactic heads and grammatical functions in the source language are mapped onto domain concepts and semantic roles in the interlingua;
a Domain Model/Ontology which defines the classes of domain concepts and restricts the fillers of semantic roles for each class;
Target Mapping Rules which indicate how domain concepts and semantic roles in the interlingua are mapped onto syntactic heads and grammatical functions in the target language;
a Target Lexicon which contains appropriate target lexemes for each domain concept;
a Target Grammar for the target language which realizes target syntactic constructions as linearized output sentences.^[10]

Advantages

No bilingual texts are required. This makes it possible to create translation systems for languages that have no texts in common, or even no digitized data whatsoever.
Domain independent. Rules are usually written in a domain independent manner, so the vast majority of rules will "just work" in every domain, and only a few specific cases per domain may need rules written for them.
No quality ceiling. Every error can be corrected with a targeted rule, even if the trigger case is extremely rare. This is in contrast to statistical systems where infrequent forms will be washed away by default.
Total control. Because all rules are hand-written, you can easily debug a rule-based system to see exactly where a given error enters the system, and why.
Reusability. Because RBMT systems are generally built from a strong source language analysis that is fed to a transfer step and target language generator, the source language analysis and target language generation parts can be shared between multiple translation systems, requiring only the transfer step to be specialized. Additionally, source language analysis for one language can be reused to bootstrap a closely related language analysis.

Shortcomings

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.
Failure to adapt to new domains. Although RBMT systems usually provide a mechanism to create new rules and extend and adapt the lexicon, changes are usually very costly and the results, frequently, do not pay off.^[11]

Related Research Articles

In linguistics, an affix is a morpheme that is attached to a word stem to form a new word or word form. The main two categories are derivational and inflectional affixes. Derivational affixes, such as un-, -ation, anti-, pre- etc., introduce a semantic change to the word they are attached to. Inflectional affixes introduce a syntactic change, such as singular into plural, or present simple tense into present continuous or past tense by adding -ing, -ed to an English word. All of them are bound morphemes by definition; prefixes and suffixes may be separable affixes.

A lexicon is the vocabulary of a language or branch of knowledge. In linguistics, a lexicon is a language's inventory of lexemes. The word lexicon derives from Greek word λεξικόν, neuter of λεξικός meaning 'of or for words'.

Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence. It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics. Typically data is collected in text corpora, using either rule-based, statistical or neural-based approaches in machine learning and deep learning.

<span class="mw-page-title-main">WordNet</span> Computational lexicon of English

WordNet is a lexical database of semantic relations between words that links words into semantic relations including synonyms, hyponyms, and meronyms. The synonyms are grouped into synsets with short definitions and usage examples. It can thus be seen as a combination and extension of a dictionary and thesaurus. While it is accessible to human users via a web browser, its primary use is in automatic text analysis and artificial intelligence applications. It was first created in the English language and the English WordNet database and software tools have been released under a BSD style license and are freely available for download from that WordNet website. Until about 2024 the English WordNet could be used as an online dictionary/lexical database, and references with links to single words could be made, but thereafter one have to download the database to use it. There are now WordNets in more than 200 languages.

Word-sense disambiguation is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition, it is usually subconscious.

Parsing, syntax analysis, or syntactic analysis is the process of analyzing a string of symbols, either in natural language, computer languages or data structures, conforming to the rules of a formal grammar. The term parsing comes from Latin pars (orationis), meaning part.

Machine translation can use a method based on dictionary entries, which means that the words will be translated as a dictionary does – word by word, usually without much correlation of meaning between them. Dictionary lookups may be done with or without morphological analysis or lemmatisation. While this approach to machine translation is probably the least sophisticated, dictionary-based machine translation is ideally suitable for the translation of long lists of phrases on the subsentential level, e.g. inventories or simple catalogs of products and services.

A symbolic linguistic representation is a representation of an utterance that uses symbols to represent linguistic information about the utterance, such as information about phonetics, phonology, morphology, syntax, or semantics. Symbolic linguistic representations are different from non-symbolic representations, such as recordings, because they use symbols to represent linguistic information rather than measurements.

A semantic lexicon is a digital dictionary of words labeled with semantic classes so associations can be drawn between words that have not previously been encountered. Semantic lexicons are built upon semantic networks, which represent the semantic relations between words. The difference between a semantic lexicon and a semantic network is that a semantic lexicon has definitions for each word, or a "gloss".

Interlingual machine translation is one of the classic approaches to machine translation. In this approach, the source language, i.e. the text to be translated is transformed into an interlingua, i.e., an abstract language-independent representation. The target language is then generated from the interlingua. Within the rule-based machine translation paradigm, the interlingual approach is an alternative to the direct approach and the transfer approach.

Statistical machine translation (SMT) is a machine translation approach where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The statistical approach contrasts with the rule-based approaches to machine translation as well as with example-based machine translation, that superseded the previous rule-based approach that required explicit description of each and every linguistic rule, which was costly, and which often did not generalize to other languages. Since 2003, the statistical approach itself has been gradually superseded by the deep learning-based neural machine translation.

Linguistic categories include

Nanosyntax is an approach to syntax where the terminal nodes of syntactic parse trees may be reduced to units smaller than a morpheme. Each unit may stand as an irreducible element and not be required to form a further "subtree." Due to its reduction to the smallest terminal possible, the terminals are smaller than morphemes. Therefore, morphemes and words cannot be itemised as a single terminal, and instead are composed by several terminals. As a result, nanosyntax can serve as a solution to phenomena that are inadequately explained by other theories of syntax.

A multiword expression (MWE), also called phraseme, is a lexeme-like unit made up of a sequence of two or more lexemes that has properties that are not predictable from the properties of the individual lexemes or their normal mode of combination. MWEs differ from lexemes in that the latter are required by many sources to have meaning that cannot be derived from the meaning of separate components. While MWEs must have some properties that cannot be derived from the same property of the components, the property in question does not need to be meaning.

Transfer-based machine translation is a type of machine translation (MT). It is currently one of the most widely used methods of machine translation. In contrast to the simpler direct model of MT, transfer MT breaks translation into three steps: analysis of the source language text to determine its grammatical structure, transfer of the resulting structure to a structure suitable for generating text in the target language, and finally generation of this text. Transfer-based MT systems are thus capable of using knowledge of the source and target languages.

Knowledge extraction is the creation of knowledge from structured and unstructured sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL, the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge or the generation of a schema based on the source data.

The mental lexicon is a component of the human language faculty that contains information regarding the composition of words, such as their meanings, pronunciations, and syntactic characteristics. The mental lexicon is used in linguistics and psycholinguistics to refer to individual speakers' lexical, or word, representations. However, there is some disagreement as to the utility of the mental lexicon as a scientific construct.

The following outline is provided as an overview of and topical guide to natural-language processing:

Machine translation (MT) algorithms may be classified by their operating principle. MT may be based on a set of linguistic rules, or on large bodies (corpora) of already existing parallel texts. Rule-based methodologies may consist in a direct word-by-word translation, or operate via a more abstract representation of meaning: a representation either specific to the language pair, or a language-independent interlingua. Corpora-based methodologies rely on machine learning and may follow specific examples taken from the parallel texts, or may calculate statistical probabilities to select a preferred option out of all possible translations.

Semantic parsing is the task of converting a natural language utterance to a logical form: a machine-understandable representation of its meaning. Semantic parsing can thus be understood as extracting the precise meaning of an utterance. Applications of semantic parsing include machine translation, question answering, ontology induction, automated reasoning, and code generation. The phrase was first used in the 1970s by Yorick Wilks as the basis for machine translation programs working with only semantic representations. Semantic parsing is one of the important tasks in computational linguistics and natural language processing.

References

↑ Wang, Haifeng; Wu, Hua; He, Zhongjun; Huang, Liang; Church, Kenneth Ward (2022-11-01). "Progress in Machine Translation". Engineering. ISSN 2095-8099.
↑ "MT Software". AAMT. Archived from the original on 2005-02-04.
↑ "MACHINE TRANSLATION IN JAPAN". www.wtec.org. January 1992. Archived from the original on 2018-02-12.
↑ Koehn, Philipp (2010). Statistical Machine Translation. Cambridge: Cambridge University Press. p. 15. ISBN 9780521874151.
↑ Nirenburg, Sergei (1989). "Knowledge-Based Machine Translation". Machine Trandation 4 (1989), 5 - 24. 4 (1). Kluwer Academic Publishers: 5–24. JSTOR 40008396.
1 2 3 Vossen, Piek: Ontologies. In: Mitkov, Ruslan (ed.) (2003): Handbook of Computational Linguistics, Chapter 25. Oxford: Oxford University Press.
↑ Knight, Kevin (1993). "Building a Large Ontology for Machine Translation". Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21–24, 1993. Princeton, New Jersey: Association for Computational Linguistics. pp. 185–190. doi: 10.3115/1075671.1075713 . ISBN 978-1-55860-324-0.
↑ Knight, Kevin; Luk, Steve K. (1994). Building a Large-Scale Knowledge Base for Machine Translation. Paper presented at the Twelfth National Conference on Artificial Intelligence. arXiv: cmp-lg/9407029 .
↑ Hettige, B.; Karunananda, A.S. (2011). "Computational Model of Grammar for English to Sinhala Machine Translation". 2011 International Conference on Advances in ICT for Emerging Regions (ICTer). pp. 26–31. doi:10.1109/ICTer.2011.6075022. ISBN 978-1-4577-1114-5. S2CID 45871137.
↑ Lonsdale, Deryle; Mitamura, Teruko; Nyberg, Eric (1995). "Acquisition of Large Lexicons for Practical Knowledge-Based MT". Machine Translation. 9 (3–4). Kluwer Academic Publishers: 251–283. doi:10.1007/BF00980580. S2CID 1106335.
↑ Lagarda, A.-L.; Alabau, V.; Casacuberta, F.; Silva, R.; Díaz-de-Liaño, E. (2009). "Statistical Post-Editing of a Rule-Based Machine Translation System" (PDF). Proceedings of NAACL HLT 2009: Short Papers, pages 217–220, Boulder, Colorado. Association for Computational Linguistics. Retrieved 20 June 2012.

Literature

Arnold, D.J. et al. (1993): Machine Translation: an Introductory Guide
Hutchins, W.J. (1986): Machine Translation: Past, Present, Future

Links

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[1] Wang, Haifeng; Wu, Hua; He, Zhongjun; Huang, Liang; Church, Kenneth Ward (2022-11-01). "Progress in Machine Translation". Engineering. ISSN 2095-8099.

[2] "MT Software". AAMT. Archived from the original on 2005-02-04.

[3] "MACHINE TRANSLATION IN JAPAN". www.wtec.org. January 1992. Archived from the original on 2018-02-12.

[4] Koehn, Philipp (2010). Statistical Machine Translation. Cambridge: Cambridge University Press. p. 15. ISBN 9780521874151.

[5] Nirenburg, Sergei (1989). "Knowledge-Based Machine Translation". Machine Trandation 4 (1989), 5 - 24. 4 (1). Kluwer Academic Publishers: 5–24. JSTOR 40008396.

[Vossen-6] 1 2 3 Vossen, Piek: Ontologies. In: Mitkov, Ruslan (ed.) (2003): Handbook of Computational Linguistics, Chapter 25. Oxford: Oxford University Press.

[7] Knight, Kevin (1993). "Building a Large Ontology for Machine Translation". Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21–24, 1993. Princeton, New Jersey: Association for Computational Linguistics. pp. 185–190. doi: 10.3115/1075671.1075713 . ISBN 978-1-55860-324-0.

[8] Knight, Kevin; Luk, Steve K. (1994). Building a Large-Scale Knowledge Base for Machine Translation. Paper presented at the Twelfth National Conference on Artificial Intelligence. arXiv: cmp-lg/9407029 .

[9] Hettige, B.; Karunananda, A.S. (2011). "Computational Model of Grammar for English to Sinhala Machine Translation". 2011 International Conference on Advances in ICT for Emerging Regions (ICTer). pp. 26–31. doi:10.1109/ICTer.2011.6075022. ISBN 978-1-4577-1114-5. S2CID 45871137.

[10] Lonsdale, Deryle; Mitamura, Teruko; Nyberg, Eric (1995). "Acquisition of Large Lexicons for Practical Knowledge-Based MT". Machine Translation. 9 (3–4). Kluwer Academic Publishers: 251–283. doi:10.1007/BF00980580. S2CID 1106335.

[11] Lagarda, A.-L.; Alabau, V.; Casacuberta, F.; Silva, R.; Díaz-de-Liaño, E. (2009). "Statistical Post-Editing of a Rule-Based Machine Translation System" (PDF). Proceedings of NAACL HLT 2009: Short Papers, pages 217–220, Boulder, Colorado. Association for Computational Linguistics. Retrieved 20 June 2012.

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