Example-based machine translation

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{theory, work, human}
{system, computer, user}
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{car, race, vehicle}

The example-based machine translation (EBMT) 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. 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.

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:

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.[1] It soon attracted the attention of scientists in the field of natural language processing.

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.

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