Getting Past the Language Gap: Innovations in Machine Translation
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 rev
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Getting Past the Language Gap: Innovations in Machine Translation Rodolfo Delmonte
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-ToSpeech 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.
R. Delmonte, Ph.D. (*) Department of Linguistic Studies and Comparative Cultures, Ca’ Foscari University, Dorsoduro 1075, Venezia 30123, Italy e-mail: [email protected]; project.cgm.unive.it
A. Neustein and J.A. Markowitz (eds.), Mobile Speech and Advanced Natural Language Solutions, DOI 10.1007/978-1-4614-6018-3_6, © Springer Science+Business Media New York 2013
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Introduction In 2005b John Hutchins wrote the following gloomy assessment of Machine Translation (MT): Machine translation (MT) is still better known for its failures than for its successes. It continues to labour under misconceptions and prejudices from the ALPAC report of more than thirty years ago, and now it has to contend with widespread misunderstanding and ridicule from users of online MT services. The goal of developing fully automatic general-purpose systems capable of near-human translation quality has been long abandoned. The aim is now to produce aids and tools for professional and non-professional translation which exploit the potentials of computers to support human skills and intelligence, or which provide rough translations for users to extract the essential information from texts in foreign languages. JH (ibid., 1–5)
Since then the field of Machine Translation (MT) has dramatically changed. And in the past 3 years, the field of
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