Using morphology to improve Example-Based Machine Translation
The case of Arabic-to-English translation
- Author(s): Violetta Cavalli-Sforza 1 and Aaron B. Phillips 2
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View Affiliations Hide AffiliationsAffiliations:1 School of Science and Engineering, Al Akhawayn University, Ifrane, Morocco2 Language Technologies Institute Carnegie Mellon University, Pittsburgh, Pennsylvania
- Source: Challenges for Arabic Machine Translation , pp 23-48
- Publication Date August 2012
We describe how morphological information was used in an Example-Based Arabic-to-English Machine Translation system to produce significant improvement in translation quality on both small and large corpora. We experimented with different methods of generalizing morphology to obtain more candidate source-side matches, while retaining information about the specific input to be translated. This information was then used with adaptation rules and a language model to generate context-appropriate target-side fragments, select and combine them. We outline essential differences between Statistical MT (SMT) and Example-based MT (EBMT), compare ourselves to other EBMT systems used with morphologically complex languages, and justify our choice of EBMT over SMT.
- Affiliations: 1: School of Science and Engineering, Al Akhawayn University, Ifrane, Morocco; 2: Language Technologies Institute Carnegie Mellon University, Pittsburgh, Pennsylvania
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