Example-based machine translation
|This article needs additional citations for verification. (June 2012)|
Example-based machine translation (EBMT) is a method of machine translation often characterized by its use of a bilingual corpus with parallel texts as its main knowledge base, at run-time. It is essentially a translation by analogy and can be viewed as an implementation of case-based reasoning approach of machine learning.
At the foundation of example-based machine translation is the idea of translation by analogy. When applied to the process of human translation, the idea that translation takes place by analogy is a rejection of the idea that people translate sentences by doing deep linguistic analysis. Instead it is founded on the belief that people translate firstly by decomposing a sentence into certain phrases, then by translating these phrases, and finally by properly composing these fragments into one long sentence. Phrasal translations are translated by analogy to previous translations. The principle of translation by analogy is encoded to example-based machine translation through the example translations that are used to train such a system.
|How much is that red umbrella?||Ano akai kasa wa ikura desu ka.|
|How much is that small camera?||Ano chiisai kamera wa ikura desu ka.|
Example-based machine translation systems are trained from bilingual parallel corpora, which contain sentence pairs like the example shown in the table. Sentence pairs contain sentences in one language with their translations into another. The particular example shows an example of a minimal pair, meaning that the sentences vary by just one element. These sentences make it simple to learn translations of subsentential units. For example, an example-based machine translation system would learn three units of translation:
- How much is that X ? corresponds to Ano X wa ikura desu ka.
- red umbrella corresponds to akai kasa
- small camera corresponds to chiisai kamera
Composing these units can be used to produce novel translations in the future. For example, if we have been trained using some text containing the sentences:
President Kennedy was shot dead during the parade. and The convict escaped on July 15th. We could translate the sentence The convict was shot dead during the parade. by substituting the appropriate parts of the sentences.
Other approaches to machine translation, including statistical machine translation, also use bilingual corpora to learn the process of translation.
Example based machine translation was first suggested by Makoto Nagao in 1984. He pointed out that the Example-based Machine Translation is especially adapted to the translation between two totally different languages, such as English and Japanese, in this case one sentence can be translated into several good structured sentences in another language, therefore, it is no use to do the deep linguistic analysis, which characters the rule-based machine translation.
Components of the Example-based Machine Translation System
- By doing the translation, the input sentence is firstly decomposed into certain fragmental phrases;
- Then, these fragmental phrases are translated into the target language phrases by the analogy translation principle with proper examples as its reference;
- Finally the target language phrases are composed into one long sentence.
Substages of Nagao’s Japanese-English translation system:
- Reduction & supplement. Both redundant and eliminated expressions will be worded to get an essential sentential structure in the source language.
- Analysis by case grammar. The Japanese input sentences will not be analyzed by Phrase Structure Grammar
- Retrieval of words & phrases in target language from the dictionary. In the dictionary you can find not only grammatical information, but also examples, meanings, case frames,etc.
- Similarity recognition. A thesaurus is used to check the similarity of the input sentential phrases and the example phrases in the dictionary.
- Choice of a global sentential form. The sentential form of translation can only be decided by the examples in the dictionary.
- Choice of local phrase structure. It will be derived from the requirement of the whole sentential structure.
EBMT is best suited for sub-language phenomena like phrasal verbs.
Phrasal verbs have highly context-dependent meanings. Phrasal verbs are a commonly occurring feature in English and comprise a verb followed by an adverb and/or a preposition. The adverb/preposition(s) are termed as the particle to the verb. Phrasal verbs produce specialized context-specific meanings that may not be derived from the meaning of the constituents. There is almost always an ambiguity during word-to-word translation from source to the target language.
As an example, let us consider the phrasal verb: put on and its Hindi meaning. It may be used in any of the following ways:
- Ram put on the lights. (Switched on) (Jalana)
- Ram put on a cap. (Wear) (Pahenna)
EBMT can be used to determine the context of the sentence.
- Makoto Nagao (1984). "A framework of a mechanical translation between Japanese and English by analogy principle". In A. Elithorn and R. Banerji. Artificial and Human Intelligence. Elsevier Science Publishers.
- Cunei - an open source platform for data-driven machine translation that grew out of research in EBMT, but also includes recent advances from the SMT field
- Marclator – a free/open-source marker-driven example-based machine translation system based on the Marker Hypothesis (TR Green (1979). "The necessity of syntax markers: Two experiments with artificial languages.". Journal of Verbal Learning and Verbal Behavior 18 (4): 481–496. doi:10.1016/S0022-5371(79)90264-0.)
- Yeminli Sözlük - An English — Turkish corpus/dictionary of millions of pre-translated sentences