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Mobile Speech and Advanced Natural Language Solutions pp 103–181Cite as

Getting Past the Language Gap: Innovations in Machine Translation

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Abstract

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT.

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Notes

  1. 1.

    Consequently, I will not be concerned with commercial versions of MT systems, nor to the field of computer-assisted Translation systems or translation aids: they are all listed at http://www.hutchinsweb.me.uk/Compendium.htm, a document produced almost on a yearly basis and compiled by John Hutchins, who is also responsible for the main source of information on MT which is regularly posted on the Machine Translation Archive (http://www.mt-archive.info).

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    Rodolfo Delmonte Ph.D.

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Delmonte, R. (2013). Getting Past the Language Gap: Innovations in Machine Translation. In: Neustein, A., Markowitz, J. (eds) Mobile Speech and Advanced Natural Language Solutions. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6018-3_6

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