A Probabilistic Model for Sign Language Translation Memory

  • Achraf OthmanEmail author
  • Mohamed Jemni
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 182)


In this paper, we present an approach for building translation memory for American Sign Language (ASL) from parallel corpora between English and ASL, by identifying new alignment combinations of words from existing texts. Our primary contribution is the application of several models of alignments for Sign Language. The model represents probabilistic relationships between properties of words, and relates them to learned underlying causes of structural variability within the domain. We developed a statistical machine translation based on generated translation memory. The model was evaluated on a big parallel corpus containing more than 800 millions of words. IBM Models have been applied to align Sign Language Corpora then we have run experimentation on a big collection of paired data between English and American Sign Language. The result is useful to build a Statistical Machine Language or any related field.


Sign Language Translation Memory Probabilistic Model 


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  1. 1.
    Morrissey, S., Way, A.: Joining hands: Developing a sign language machine translation system with and for the deaf community. In: Proceeding CVHI Conference, Workshop Assistive Technol. People with Vision and Hearing Impairments, Granada, Spain (2007)Google Scholar
  2. 2.
    Morrissey, S.: Assistive technology for deaf people: Translating into and animating Irish sign language. In: Proceeding Young Researchers Consortium, ICCHP, Linz, Austria (2008)Google Scholar
  3. 3.
    American Sign Language Gloss Parallel Corpus 2012, ASLG-PC12 (2012),
  4. 4.
    Gutenberg Project (2012),
  5. 5.
    Koehn, P., Och, F., Marcu, D.: Statistical phrase-based translation. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, NAACL 2003 (2003)Google Scholar
  6. 6.
    Koehn, P.: Statistical Machine Translation. Cambridge University Press (2009)Google Scholar
  7. 7.
    Brown, P., Pietra, V., Pietra, S., Mercer, R.: The mathematics of statistical machine translation: parameter estimation. Computational Linguistics, Special issue on using large corpora: II (1993)Google Scholar
  8. 8.
    Huenerfauth, M., Zhou, L., Gu, E., Allbeck, J.: Evaluation of American sign language generation by native ASL signers. ACM Transaction Accessible Computing (2008)Google Scholar
  9. 9.
    Marshall, I., Sáfár, E.: Sign language generation using HPSG. In: Proceeding 9th International Conference Theoretical Methodological Issues Machine Translation, TMI 2002, Keihanna, Japan (2002)Google Scholar
  10. 10.
    Othman, A., Jemni, M.: English-ASL Gloss Parallel Corpus 2012: ASLG-PC12. In: 5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon LREC 2012, Istanbul, Turkey (2012)Google Scholar
  11. 11.
    Jemni, M., Elghoul, O., Makhlouf, S.: A Web-Based Tool to Create Online Courses for Deaf Pupils. In: International Conference on Interactive Mobile and Computer Aided Learning, Amman, Jordan (2007)Google Scholar
  12. 12.
    Jemni, M., Elghoul, O.: An avatar based approach for automatic interpretation of text to Sign language. In: 9th European Conference for the Advancement of the Assistive Technologies in Europe, San Sebastián, Spain (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Research Laboratory LaTICEUniversity of TunisTunisTunisia

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