• George TambouratzisEmail author
  • Marina Vassiliou
  • Sokratis Sofianopoulos
Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)


This chapter contains a general introduction to the topic of the present book. It presents the current challenges of Machine Translation (MT), in particular for languages where only a limited amount of specialised resources is readily available. To that end, a comprehensive review of the state-of-the-art in MT is performed. Focus is placed on related work on MT methodologies that are portable to new language pairs, and issues such as stability and extensibility are emphasised. It is widely accepted that language portability necessitates an algorithmic approach to extract information from large corpora in an unsupervised manner. This includes both Statistical MT (SMT) and Example-based MT (EBMT). Here, a review of the strengths and shortcomings of the different approaches is performed, in terms of the a priori externally-provided linguistic knowledge and required specialised resources. This review leads to the concept of the proposed MT methodology.


Example-based MT (EBMT) Language Pairs Machine Translation (MT) Rule-based MT (RBMT) Presemt 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Arnold D (1986) Eurotra: a European Perspective on MT. Proc IEEE 74(7):979–992Google Scholar
  2. Arnold D, Sadler L (1992) EUROTRA: an assessment of the current state of the EC’s MT programme. In: Translation and the computer, 13: the theory and practice of machine translation—a marriage of convenience? ASLIB, LondonGoogle Scholar
  3. Brown PF, Della Pietra SA, Della Pietra VJ, Mercer RL (1993) The mathematics of statistical machine translation: parameter estimation. Comput Linguist 19(2):263–311Google Scholar
  4. Carbonell J, Klein S, Miller D, Steinbaum M, Grassiany T, Frey J (2006) Context-based machine translation. In: Proceedings of the 7th AMTA Conference, Cambridge, MA, USA, pp 19–28Google Scholar
  5. Costa-jussà MR, Banchs R, Rapp R, Lambert P, Eberle K, Babych B (2013) Workshop on hybrid approaches to translation: overview and developments. In: Proceedings of the 2nd HYTRA workshop, held within ACL-2013, Sofia, Bulgaria, pp 1–6Google Scholar
  6. Dologlou Y, Markantonatou S, Tambouratzis G, Yannoutsou O, Fourla S, Ioannou N (2003) Using monolingual corpora for statistical machine translation: the METIS system. In: Proceedings of the EAMT-CLAW’03 Workshop, Dublin, Ireland, 15–17 May, pp 61–68Google Scholar
  7. Eisele A, Federmann C, Uszkoreit H, Saint-Amand H, Kay M, Jellinghaus M, Hunsicker S, Herrmann T, Chen Y (2008) Hybrid machine translation architectures within and beyond the EuroMatrix project. In: Hutchins J, v.Hahn W (eds) Proceedings of EAMT 2008 Conference, 22–23 September 2008, Hamburg, Germany, pp 27–34Google Scholar
  8. Forcada ML, Ginestí-Rosell M, Nordfalk J, O’Regan J, Ortiz-Rojas S, Pérez-Ortiz JA, Sánchez-Martínez F, Ramírez-Sánchez G, Tyers FM (2011) Apertium: a free/open-source platform for rule-based machine translation. Mach Transl 25:127–144CrossRefGoogle Scholar
  9. Hutchins J (1996) ALPAC: the (in)famous report. MT News Int 14:9–12Google Scholar
  10. Hutchins J (2005) Example-based machine translation: a review and commentary. Mach Transl 19:197–211CrossRefGoogle Scholar
  11. Jurafsky D, Martin JH (2009) Speech and language processing: an introduction to natural language processing. In: Computational linguistics and speech recognition, 2nd edn. Pearson Educational, Upper Saddle River, pp 895–944. ISBN 978-0-13-504196-1Google Scholar
  12. Klementiev A, Irvine A, Callison-Burch C, Yarowsky D (2012) Toward statistical machine translation without parallel corpora. In: Proceedings of EACL2012, Avignon, France, 23–25 April, pp. 130–140Google Scholar
  13. Koehn P (2010) Statistical machine translation. Cambridge University Press. xii, 433 pp. ISBN 978-0-521-87415-1Google Scholar
  14. Koehn P, Knight K (2002) Learning a translation lexicon from monolingual corpora. In: Proceedings of the ACL-02 workshop on Unsupervised lexical acquisition, Philadelphia, Pennsylvania, U.S.A., 12 July 2002, pp. 9–16Google Scholar
  15. Koehn P, Hoang H, Birch A, Callison-Burch C, Federico M, Bertoldi N, Cowan B, Shen W, Moran C, Zens R, Dyer C, Bojar O, Constantin A, Herbst E (2007) Moses: open source toolkit for statistical machine translation. In: ACL 2007: proceedings of demo and poster sessions, Prague, Czech Republic, June 2007, pp 177–180Google Scholar
  16. Markantonatou S, Sofianopoulos S, Yannoutsou O, Vassiliou M (2009) Hybrid machine translation for low- and middle-density languages. In: Nirenburg S (ed) Language engineering for lesser-studied languages. IOS Press, pp 243–274. ISBN 978-1-58603-954-7Google Scholar
  17. Nagao M (1984) A framework of a mechanical translation between Japanese and English by analogy principle. In: Elithorn A, Banerji R (eds) Artificial and human intelligence: edited review papers presented at the international NATO Symposium, October 1981, Lyons, France. Amsterdam: North Holland, pp 173–180Google Scholar
  18. Quirk C, Menezes A (2006) Dependency treelet translation: the convergence of statistical and example-based machine translation? Mach Transl 20:45–66Google Scholar
  19. Sánchez-Cartagena VM, Pérez-Ortiz JA, Sánchez-Martínez F (2015) A generalised alignment template formalism and its application to the inference of shallow-transfer machine translation rules from scarce bilingual corpora. Comput Speech Lang (Special Issue on Hybrid Machine Translation) 32(1):49–90Google Scholar
  20. Senellart J, Dienes P, Varadi T (2001) New generation SYSTRAN system. In: Proceedings of the 8th MT Summit, 18–22 September, Santiago de Compostella, Spain, pp 311–316Google Scholar
  21. Surcin S, Lange E, Senellart J (2007) Rapid development of new language pairs at SYSTRAN. In: Proceedings of MT Summit XI, 10–14 September, Copenhagen, Denmark, pp 443-449Google Scholar
  22. Su J, Wu H, Wang H, Chen Y, Shi X, Dong H, Liu Q (2012) Translation Model Adaptation for Statistical Machine Translation with Monolingual Topic Information. In: Proceedings of ACL2012 Conference, Jeju, Republic of Korea, July 2012, pp 459–468Google Scholar
  23. Wu D (2005) MT model space: statistical versus compositional versus example-based machine translation. Mach Transl 19:213–227CrossRefGoogle Scholar
  24. Yang J, Enoue S, Senellart J, Croiset T (2009) SYSTRAN Chinese-English and English-Chinese hybrid machine translation systems. In: Proceedings of CWMT-2009: the 5th China Workshop on Machine Translation, Nanjing, China, 16–17 October, p 8Google Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  • George Tambouratzis
    • 1
    Email author
  • Marina Vassiliou
    • 1
  • Sokratis Sofianopoulos
    • 1
  1. 1.Institute for Language and Speech ProcessingAthensGreece

Personalised recommendations