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.

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  • 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: 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 Nagao Makoto in 1984. It soon attracted the attention of scientists in the field of natural language processing.
  • Traducción automática basada en ejemplos (en inglés Example-based machine translation, EBMT) es un enfoque de la traducción automática en el que se usan corpora de ejemplos como base de conocimiento principal. En la base de este método está la idea de que se pueden traducir textos por analogía. Cuándo lo aplicamos al proceso de traducción por un humano, presuponemos que la traducción no se hace por análisis lingüístico profundo sino por sustitución de frases. Es decir, se reduce la oración en la lengua original a sintagmas, se traducen estos sintagmas y después dichos sintagmas se integran en la oración meta. Se entrenan sistemas de traducción automática basada en ejemplos con corpora alineados que contienen pares de oraciones (véase el ejemplo en el cuadro). Los pares de oraciones contienen oraciones en un idioma y traducciones en otro. En este ejemplo, vemos un par mínimo porque las oraciones se distinguen por un solo elemento. Con estos tipos de oraciones es muy fácil aprender traducciones de unidades suboracionales. Por ejemplo, una sistema aprende tres unidades de traducción: How much is that X ? coincide con Ano X wa ikura desu ka. red umbrella coincide con akai kasa small camera coincide con chiisai kamera Mediante estas unidades, se pueden construir traducciones nuevas. Por ejemplo, si usamos un texto para entrenar el sistema que tiene las oraciones: President Kennedy was shot dead during the parade. y The convict escaped on July 15th. Sería posible traducir la oración The convict was shot dead during the parade. por sustitución de unidades suboracionales.
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  • 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.
  • Traducción automática basada en ejemplos (en inglés Example-based machine translation, EBMT) es un enfoque de la traducción automática en el que se usan corpora de ejemplos como base de conocimiento principal. En la base de este método está la idea de que se pueden traducir textos por analogía. Cuándo lo aplicamos al proceso de traducción por un humano, presuponemos que la traducción no se hace por análisis lingüístico profundo sino por sustitución de frases.
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  • Example-based machine translation
  • Traducción automática basada en ejemplos
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