Enhancing English-Japanese Translation Using Syntactic Pattern Recognition Methods

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 578)

Abstract

In this paper, we present a novel approach to Machine Translation (MT) using syntactic Pattern Recognition (PR) methods. Our aim is to evaluate the possibility of using syntactic PR techniques in this mature field, and to identify any potential benefits that can be gleaned by such an approach. To make use of syntactic PR techniques, we propose a system that performs string-matching to pair English sentence structures to Japanese (The specific languages, namely English and Japanese, were chosen because their sentence structures are completely dissimilar. This, however, proves the point that such syntactic methods will be applicable for other pairs of languages too.) structures – as opposed to matching strings in and of themselves, and to thus facilitate translation between the languages. In order to process the sentence structures of either language as a string, we have created a representation that replaces the tokens of a sentence with their respective Part-of-Speech tags. Further, to perform the actual string-matching operation, we make use of the OptPR algorithm, a syntactic award-winning PR scheme that has been proven to achieve optimal accuracy, and that also attains the information theoretic bound. Through our experiments, we show that our implementation obtains superior results to that of a standard statistical MT system on our data set. Our results provide the additional guarantee of generating a known sentence structure in the target language. With further research, this system could be expanded to have a more complete coverage of the languages worked with. The incorporation of such PR techniques in MT, in general, and the OptPR algorithm, in particular, are both pioneering.

Keywords

Machine Translation (MT) Syntactic Methods in MT 

References

  1. 1.
    Baldridge, J.: The OpenNLP Project (2005). http://opennlp.apache.org/index.html
  2. 2.
    Fujinami, T., Nanz, C.: The 101 Translation Problems between Japanese and German/English. University Stuttgart (1997)Google Scholar
  3. 3.
    Hindle, D., Rooth, M., Ambiguity, S., Relations, L.: Structural ambiguity and lexical relations. In: Computational Linguistics - Special Issue on Using Large Corpora, Cambridge, USA, vol. 19, 103–120 (1993)Google Scholar
  4. 4.
    Hutchins, J.: Towards a definition of example-based machine translation. In: Proceedings of Workshop on Example-Based Machine Translation, Phuket, Thailand, pp. 63–70, September 2005Google Scholar
  5. 5.
    Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)MATHGoogle Scholar
  6. 6.
    McMahon, T.: Enhancing Machine Translation for English-Japanese Using Syntactic Pattern Recognition Methods. MCS Thesis, School of Computer Science, Carleton University, Ottawa, Canada, May 2015Google Scholar
  7. 7.
    McMahon, T., Oommen, B.J.: On the Use of Syntactic Pattern Recognition Methods to Enhance English-Japanese Machine Translation. Unabridged version of this paper. Submitted for PublicationGoogle Scholar
  8. 8.
    Nagao, M.: A framework of a mechanical translation between Japanese and English by analogy principle. In: Proceedings of the International NATO Symposium on Artificial and Human Intelligence, New York, USA, pp. 173–180 (1984)Google Scholar
  9. 9.
    Oommen, B.J., Kashyap, R.L.: A formal theory for optimal and information theoretic syntactic pattern recognition. Pattern Recogn. 31, 1159–1177 (1998)CrossRefGoogle Scholar
  10. 10.
    Ho Tatoeba, T., et al.: Collecting Example Sentences (2014). http://tatoeba.org/eng/home

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of Computer ScienceCarleton UniversityOttawaCanada

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