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LRC announces 2010 Best Thesis Winner
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| On 14 September 2010, the expert panel invited by the Localisation Research Centre (LRC) to select the winner of the 14th Annual LRC Best Thesis Award met to review the submissions received by the LRC. The panel session was facilitated by the LRC and Symantec Corporation and supported by the Centre for Next Generation Localisation (CNGL).
The LRC expressed its gratitude to the panel members for making their time and their industrial and academic expertise available to the panel over the past years. The LRC thanked Symantec Ireland for its generous sponsorship of the award for the fourteenth consecutive year. Symantec’s generous and consistent sponsorship has made this award one of the most successful and longest running awards in the industry. The LRC is also very happy to once again, acknowledge the support of the Centre for Next Generation Localisation (CNGL). LRC Best Thesis Award 2010 As with the 2009 entries for the Best Thesis Award, the judging panel for 2010 were again impressed to find an increase in the number of very high quality submissions for this year’s award. Although this made choosing an outright winner more difficult, the panel unanimously agreed that the high quality of entries made the actual judging process an interesting and rewarding experience. The author of this year’s winning thesis addresses issues arising from the lack of available syntactically annotated training data for many languages in the area of syntax-based approaches to data-driven machine translation. In a very comprehensive and well presented thesis the author proposes a solution to the problem of limited resources for syntax-based MT by introducing a novel sub-sentential alignment algorithm for the induction of translational equivalence links between pairs of phrase structure trees. This algorithm, which operates on a language pair-independent basis, allows for the automatic generation of large-scale parallel treebanks which are useful not only for machine translation, but also across a variety of natural language processing tasks. The title of the Fourteenth Annual LRC Best Thesis is Resourcing Machine Translation with Parallel Treebanks, a dissertation submitted to Dublin City University in fulfilment of the requirements for the award of Doctor of Philosophy (Ph.D.), under the supervision of Prof Andy Way. The author of this thesis and the winner of the 2010 LRC Best Thesis Award is John Tinsley. The panel and the LRC would like to congratulate John on his outstanding work and wish him every success for the future. For more information on the LRC Bes Thesis Award click here or to read John's Thesis click here |
