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With or Without Meaning? Hype Cycles in Language Technology and What We Can Learn from Them

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Future and Emerging Trends in Language Technology. Machine Learning and Big Data (FETLT 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10341))

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Despite its relatively short period of existence as a scientific area, natural language processing has gone through a succession of diverse mainstream research paradigms. How similar are these inflection moments in the history of the research on language technology? What can we learn from that similarity, if any, about the overall shape of the evolution of this field? And importantly, what can we anticipate from this shape, if any, about the future and emerging trends in language technology? — which is the topic of the workshop where this paper was presented.

The result of this study is meant to be of help to organize research agendas of centers, laboratories and individual researchers and innovators, as well as to guide informed institutional funding and support for research and innovation in language technology.

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  1. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)

    Article  Google Scholar 

  2. Levy, O., Goldberg, Y., Dagan, I.: Improving distributional similarity with lessons learned from word embeddings. Trans. Assoc. Comput. Linguist. 3, 211–225 (2015)

    Google Scholar 

  3. Agirre, E., Alfonseca, E., Hall, K., Kravalova, J., Pas, M., Soroa, A.: A study on similarity and relatedness using distributional and WordNet-based approaches. In: Proceedings of Human Language Technologies: 2009 Annual Conference of the North American Chapter of the ACL, Boulder, pp. 19–27, June 2009

    Google Scholar 

  4. Richens, R.: Interlingual machine translation. Comput. J. 1(3), 144–147 (1958)

    Article  Google Scholar 

  5. Montague, R.: Universal grammar. Theoria 36(3), 373–398 (1970)

    Article  MATH  MathSciNet  Google Scholar 

  6. Copestake, A., Flickinger, D., Pollard, C., Sag, I.: Minimal recursion: an introduction. J. Res. Lang. Comput. 3(4), 281–332 (2005)

    Article  Google Scholar 

  7. Weaver, W.: Translation. The Rockefeller Foundation (1949)

    Google Scholar 

  8. Brown, P., Cocke, J., Pietra, S.D., Pietra, V.D., Jelinek, F., Lafferty, J., Mercer, R., Roossin, P.: A statistical approach to MT. Comput. Linguist. 16(2), 79–85 (1990)

    Google Scholar 

  9. Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948)

    Article  MATH  MathSciNet  Google Scholar 

  10. Koehn, P.: Statistical Machine Translation. Cambridge University Press, Cambridge (2010)

    MATH  Google Scholar 

  11. Erk, K.: Vector space models of word meaning and phrase meaning: a survey. Lang. Linguist. Compass 6(10), 635–653 (2012)

    Article  Google Scholar 

  12. Koehn, P., Hong, H.: Factored translation models. In: Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Prague, pp. 868–876, June 2007

    Google Scholar 

  13. Sustekever, I., Vinyals, O., Lee, Q.: Sequence to sequence learning with neural networks. In: Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS 2014), Montreal, 8–13 December 2014, pp. 3104–3112 (2014)

    Google Scholar 

  14. Johnson, M., Schuster, M., Le, Q., Krikun, M., Yonghui, W., Chen, Z., Thorat, N.: Google’s multilingual neural machine translation system: Enabling zero-shot translation (2016). arXiv:1611.04558v1

  15. Harris, Z.: Distributional structure. Word 10(2–3), 146–162 (1954)

    Article  Google Scholar 

  16. McCulloch, W., Pitts, W.: A logical calculus of the ideas immanent in nervous systems. Bull. Math. Biophys. 5, 115–133 (1943)

    Article  MATH  MathSciNet  Google Scholar 

  17. Devlin, J., Zbib, R., Huang, Z., Lamar, T., Schwartz, R., Makhoul, J.: Fast and robust neural network joint models for statistical machine translation. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, 23–25 June 2014, pp. 1370–1380 (2014)

    Google Scholar 

  18. Dong, L., Lapata, M.: Language to logical form with neural attention. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, 7–12 August 2016, pp. 33–43 (2016)

    Google Scholar 

  19. Del Gaudio, R., Labaka, G., Agirre, E., Osenova, P., Simov, K., Popel, M., Oele, D., van Noord, G., Gomes, L., Rodrigues, J.A., Neale, S., Silva, J., Querido, A., Rendeiro, N., Branco, A.: SMT and hybrid systems of the QTLeap project in the WMT16 IT-task. In: Proceedings of the ACL 2016 First Conference on Machine Translation (WMT 2016), Association for Computational Linguistics, Berlin, 11–12 August 2016, pp. 435–441 (2016)

    Google Scholar 

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The work reported in this paper was partially supported by P2020 Program under the grant 08/SI/2015/3279 for the project ASSET-Intelligent Assistance for Everyone Everywhere.

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Correspondence to António Branco .

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Branco, A. (2017). With or Without Meaning? Hype Cycles in Language Technology and What We Can Learn from Them. In: Quesada, J., Martín Mateos , FJ., López Soto, T. (eds) Future and Emerging Trends in Language Technology. Machine Learning and Big Data. FETLT 2016. Lecture Notes in Computer Science(), vol 10341. Springer, Cham.

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  • Print ISBN: 978-3-319-69364-4

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