×

Apparatus for providing feedback of translation quality using concept-based back translation

  • US 7,848,915 B2
  • Filed: 08/09/2006
  • Issued: 12/07/2010
  • Est. Priority Date: 08/09/2006
  • Status: Expired due to Fees
First Claim
Patent Images

1. A concept-based back translation system, comprising:

  • a target language semantic parser module, which uses statistical techniques including decision trees to train a parser based on annotated corpus in the target language, parses a translated target sentence with the parser, once it has been trained, to extract semantic concepts, and based on results of parsing the translated target sentence, obtains a backward translation of the translated sentence into a source language;

    a source language semantic parser module, which parses a source language sentence to extract semantic concepts and uses results of parsing the source language sentence to determine a significance and relevance of the extract concepts generated in the target language sentence;

    a bi-directional machine translation module, which receives a translated target language sentence and converts the translated target language sentence back into the source language;

    a relevancy judging module, which measures a significance and a relevance of the back translation by semantically comparing the back translation with an original source language sentence; and

    a back translation display module, which displays the back translation dynamically based on the relevance and the significance of the back translationwherein the relevancy judging module measures the relevancy using a semantically weighted score based on modified n-gram precision where the original source language sentence is treated as a reference, and the back translation is treated as a translation hypothesis,wherein the relevancy between the hypothesis and the reference are compared based on a modified n-gram precision, which automatically evaluates translation performance, andwherein a higher weight is assigned to n-grams that contain significant semantic annotations, and a lower weight is assigned to n-grams that contain no significant semantic role, the significance of the n-grams is judged based on semantic parsing results, and on a pre-defined list of key concepts for an application domain.

View all claims
  • 1 Assignment
Timeline View
Assignment View
    ×
    ×