Conclusions and Future Work

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


This chapter performs a review of the research work discussed in the previous chapters of the present volume. This review represents a summary of the outcomes of the research within the PRESEMT project. As a logical outcome, a set of key directions is identified for future work in order to further improve the MT methodology. A brief report of the most promising ones is provided in the second part of this chapter.


  1. Black PE (2005) Dictionary of algorithms and data structures. U.S. National Institute of Standards and Technology (NIST)Google Scholar
  2. Fritzke B (1995) A growing neural gas network learns topologies. In: Advances in neural information processing systems, vol 7. MIT Press, Cambridge, pp 625–632Google Scholar
  3. Gispert A, Iglesias G, Byrne B (2015) Fast and accurate preordering for SMT using neural networks. In: NAACL-HLT-2015 ConferenceGoogle Scholar
  4. Klementiev A, Irvine A, Callison-Burch C, Yarowsky D (2012) Toward statistical machine translation without parallel corpora. In: Proceedings of EACL-2012, Avignon, France, 23–25 April, pp 130–140Google Scholar
  5. Koehn P, Knight K (2002) Learning a translation lexicon from monolingual corpora. In: ACL Workshop on Unsupervised Lexical AcquisitionGoogle Scholar
  6. Kohonen T (1997) Self-Organising Maps (2nd ed.) Berlin, Springer-VerlagGoogle Scholar
  7. Kohonen T, Kaski S, Lagus K, Salojarvi J, Honkela J, Paatero V, Saarela A (2000) Self organisation of a massive document collection. IEEE Trans Neural Networks 11(3):574–585CrossRefGoogle Scholar
  8. Lerner U, Petrov S (2013) Source-side classifier preordering for machine translation. In: Proceedings of EMNLP-2013 Conference, Seattle, USA, October 2013, pp 513–523Google Scholar
  9. Lynum A, Marsi E, Bungum L, Gambäck B (2012) Disambiguating word translations with target language models. In: Text, speech and dialogue: Proceedings of the TSD-2012 International Conference, Brno, Czech Republic, 3–7 September. Springer, Brno, pp 378–385Google Scholar
  10. Nuhn M, Mauser A, Ney H (2012) Deciphering foreign language by combining language models and context vectors. In: Proceedings of the ACL-2012 Conference, Jeju, Republic of Korea, 8–14 July, pp 156–1643Google Scholar
  11. Petrov S, Das D, McDonald R (2012) A universal part-of-speech tagset. In: Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC ‘12), 23–25 May, Istanbul, TurkeyGoogle Scholar
  12. Rauber A, Merkl D, Dittenbach M (2002) The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data. IEEE Trans Neural Networks 13(6):1331–1341CrossRefzbMATHGoogle Scholar
  13. Siivola V, Pellom BL (2005) Growing an n-gram language model. In Proceedings of Interspeech’05 Conference, Lisbon, Portugal, pp 1309–1312Google Scholar
  14. Siu M, Ostendorf M (2000) Variable n-gram language modeling and extensions for conversational speech. IEEE Trans Speech Audio Process 8(1):63–75CrossRefGoogle Scholar
  15. Stymne S (2009) A comparison of merging strategies for translation of German compounds. In: Proceedings of EACL, Student Research Workshop, Athens, Greece, pp 61–69Google Scholar
  16. Stymne S (2012) Clustered word classes for preordering in statistical machine translation. In: Proceedings of the 13th EACL Conference, 23–27 April, Avignon, France, pp 28–34Google Scholar
  17. Tambouratzis G, Pouli V (2015) Establishing sentential structure via realignments from small parallel corpora. In: Proceedings of HYTRA-2015 Workshop, held within ACL/IJCNLP-2015, Beijing, China, 31 July, pp 21–29Google Scholar
  18. Tambouratzis G, Pouli V (2016) Linguistically inspired language model augmentation for MT. In: Proceedings of LREC-2016, 23-28 May 2016, Portoroz, Slovenia. ISBN 978-2-9517408-9-1Google Scholar
  19. Wang R, Zhao H, Lu B-L, Utiyama M, Sumita E (2014) Neural network based bilingual language model growing for statistical machine translation. In: Proceedings of the EMNLP-2014 Conference, Doha, Qatar, 25–29 October, pp 189–195Google Scholar
  20. Xia F, McCord M (2004) Improving a statistical MT system with automatically learned rewrite patterns. In: Proceedings of Coling 2004, Geneva, Switzerland, August 23–27, pp 508–514Google 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