A Dynamic Programming Approach to Improving Translation Memory Matching and Retrieval Using Paraphrases

  • Rohit Gupta
  • Constantin Orăsan
  • Qun Liu
  • Ruslan Mitkov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9924)

Abstract

Translation memory tools lack semantic knowledge like paraphrasing when they perform matching and retrieval. As a result, paraphrased segments are often not retrieved. One of the primary reasons for this is the lack of a simple and efficient algorithm to incorporate paraphrasing in the TM matching process. Gupta and Orăsan [1] proposed an algorithm which incorporates paraphrasing based on greedy approximation and dynamic programming. However, because of greedy approximation, their approach does not make full use of the paraphrases available. In this paper we propose an efficient method for incorporating paraphrasing in matching and retrieval based on dynamic programming only. We tested our approach on English-German, English-Spanish and English-French language pairs and retrieved better results for all three language pairs compared to the earlier approach [1].

Keywords

Edit distance with paraphrasing Translation memory TM matching and retrieval Computer aided translation Paraphrasing 

References

  1. 1.
    Gupta, R., Orăsan, C.: Incorporating paraphrasing in translation memory matching and retrieval. In: Proceedings of the European Association of Machine Translation (EAMT-2014) (2014)Google Scholar
  2. 2.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Sov. Phys. Dokl. 10, 707–710 (1966)MathSciNetMATHGoogle Scholar
  3. 3.
    Planas, E., Furuse, O.: Formalizing translation memories. In: Proceedings of the 7th Machine Translation Summit, pp. 331–339 (1999)Google Scholar
  4. 4.
    Macklovitch, E., Russell, G.: What’s been forgotten in translation memory. In: White, J.S. (ed.) AMTA 2000. LNCS (LNAI), vol. 1934, pp. 137–146. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Somers, H.: Translation memory systems. Comput. Transl.: Transl. Guide 35, 31–48 (2003)CrossRefGoogle Scholar
  6. 6.
    Hodász, G., Pohl, G.: MetaMorpho TM: a linguistically enriched translation memory. In: International Workshop, Modern Approaches in Translation Technologies (2005)Google Scholar
  7. 7.
    Pekar, V., Mitkov, R.: New generation translation memory: content-sensivite matching. In: Proceedings of the 40th Anniversary Congress of the Swiss Association of Translators, Terminologists and Interpreters (2007)Google Scholar
  8. 8.
    Mitkov, R.: Improving third generation translation memory systems through identification of rhetorical predicates. In: Proceedings of LangTech 2008 (2008)Google Scholar
  9. 9.
    Clark, J.P.: System, method, and product for dynamically aligning translations in a translation-memory system, 5 February 2002. US Patent 6,345,244Google Scholar
  10. 10.
    Utiyama, M., Neubig, G., Onishi, T., Sumita, E.: Searching translation memories for paraphrases. In: Machine Translation Summit XIII, pp. 325–331 (2011)Google Scholar
  11. 11.
    Gupta, R., Orăsan, C., Zampieri, M., Vela, M., Van Genabith, J.: Can translation memories afford not to use paraphrasing? In: Proceedings of EAMT (2015)Google Scholar
  12. 12.
    Timonera, K., Mitkov, R.: Improving translation memory matching through clause splitting. In: Proceedings of the Workshop on Natural Language Processing for Translation Memories (NLP4TM), Hissar, Bulgaria, pp. 17–23 (2015)Google Scholar
  13. 13.
    Ganitkevitch, J., Benjamin, V.D., Callison-Burch, C.: PPDB: the paraphrase database. In: Proceedings of NAACL-HLT, Atlanta, Georgia, pp. 758–764 (2013)Google Scholar
  14. 14.
    Steinberger, R., Eisele, A., Klocek, S., Pilos, S., Schlüter, P.: DGT-TM: a freely available translation memory in 22 languages. In: LREC, pp. 454–459 (2012)Google Scholar
  15. 15.
    Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., et al.: Moses: open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, pp. 177–180. Association for Computational Linguistics (2007)Google Scholar
  16. 16.
    Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the ACL, pp. 311–318 (2002)Google Scholar
  17. 17.
    Denkowski, M., Lavie, A.: Meteor universal: language specific translation evaluation for any target language. In: Proceedings of the EACL 2014 Workshop on Statistical Machine Translation (2014)Google Scholar
  18. 18.
    Gupta, R.: Use of language technology to imporve matching and retrieval in translation memory. Ph.D. thesis, University of Wolverhampton (2016)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Rohit Gupta
    • 1
  • Constantin Orăsan
    • 1
  • Qun Liu
    • 2
  • Ruslan Mitkov
    • 1
  1. 1.University of WolverhamptonWolverhamptonUK
  2. 2.Dublin City UniversityDublinIreland

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