Departamento de
Traducción e Interpretación


Tema:   Automática.
Autor:   Pécheux, Nicolas
Año:   2016
Título:   Modèles exponentiels et contraintes sur les espaces de recherche en traduction automatique et pour le transfert cross-lingue [Log-linear Models and Search Space Constraints in Statistical Machine Translation and Cross-lingual Transfer]
Lugar:   Paris
Editorial/Revista:   Université Paris-Saclay & Université Paris-Sud (Paris XI)
Páginas:   241
Idioma:   Francés
Tipo:   Tesis.
Disponibilidad:   Acceso abierto
Resumen:   Most natural language processing tasks are modeled as prediction problems where one aims at finding the best scoring hypothesis from a very large pool of possible outputs. Even if algorithms are designed to leverage some kind of structure, the output space is often too large to be searched exaustively. This work aims at understanding the importance of the search space and the possible use of constraints to reduce it in size and complexity. We report in this thesis three case studies which highlight the risk and benefits of manipulating the seach space in learning and inference.When information about the possible outputs of a sequence labeling task is available, it may seem appropriate to include this knowledge into the system, so as to facilitate and speed-up learning and inference. A case study on type constraints for CRFs however shows that using such constraints at training time is likely to drastically reduce performance, even when these constraints are both correct and useful at decoding.On the other side, we also consider possible relaxations of the supervision space, as in the case of learning with latent variables, or when only partial supervision is available, which we cast as ambiguous learning. Such weakly supervised methods, together with cross-lingual transfer and dictionary crawling techniques, allow us to develop natural language processing tools for under-resourced languages. Word order differences between languages pose several combinatorial challenges to machine translation and the constraints on word reorderings have a great impact on the set of potential translations that is explored during search. We study reordering constraints that allow to restrict the factorial space of permutations and explore the impact of the reordering search space design on machine translation performance. However, we show that even though it might be desirable to design better reordering spaces, model and search errors seem yet to be the most important issues. [Source: Author]
2001-2019 Universidad de Alicante DOI: 10.14198/bitra
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European Society for Translation Studies Ministerio de Educación Ivitra : Institut Virtual Internacional de Traducció asociación ibérica de estudios de traducción e interpretación