Algorithms and frameworks for Tree-based Machine Translation and tree structures prediction

Diplôme Thèse Algorithms and frameworks for Tree-based Machine Translation and tree structures prediction eng 2014-07-22 author Gesmundo Andréa 687855 director Henderson James 529988 10.13097/archive-ouverte/unige:75280 urn:nbn:ch:unige-752802 Sc. 4809 This thesis focuses on Hierarchical Machine Translation (HMT). HMT allows to model long distance phenomena and reordering at a cost of higher complexity of the decoder. With this thesis we reduce the complexity of HMT. In the first part we propose a series of alternative Cube Pruning (CP) algorithms that leverage on more aggressive pruning and less memory usage. Then we propose a linear time CP that solves exactly a relaxation of the decoding problem. All these algorithms can substitute the standard CP algorithm in any of its applications. In the second part of the thesis we present a novel Structured Prediction approach to HMT. The proposed model builds the structures incrementally, by choosing a single action at each step, and pruning all incompatible alternatives to that action. This approach allows translations to be constructed in an undirectional manner, thus not being constrained by the bottomup ordering of CKYlike algorithms. Dewey 025.063 Dewey 410 13 Centre universitaire d'informatique 0D3 32 Département d'informatique 18 82 Département de linguistique 39 false 730 Geneva Artificial Intelligence Laboratory 722 Laboratoire d'Analyse et de Traitement du Langage (LATL) 83064eb9-befb-49ca-85be-56917a461cc2 Thesis Public GesmundoThese.pdf 2563794 application/pdf openAccess Centres et instituts / Centre universitaire d'informatique Faculté des sciences / Département d'informatique Faculté des lettres / Département de linguistique

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Algorithms and frameworks for Tree-based Machine Translation and tree structures prediction				2014	999	205

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Gesmundo, Andréa