Departamento de
Traducción e Interpretación


Tema:   Automática.
Autor:   Gispert Ramís, Adrià
Año:   2007
Título:   Introducing linguistic knowledge into statistical machine translation
Lugar:   Barcelona
Editorial/Revista:   Universitat Politècnica de Catalunya
Idioma:   Inglés.
Tipo:   Tesis.
ISBN/ISSN/DOI:   ISBN: 9788469055632.
Disponibilidad:   Acceso abierto.
Resumen:   This Ph.D. thesis dissertation addresses the use of morphosyntactic information in order to improve the performance of Statistical Machine Translation (SMT) systems, providing them with additional linguistic information beyond the surface level of words from parallel corpora.
The statistical machine translation system in this work here follows a tuple-based approach, modelling joint-probability translation models via log-linear combination of bilingual n-grams with additional feature functions. A detailed study of the approach is conducted. This includes its initial development from a speech-oriented Finite-State Transducer architecture implementing X-grams towards a large-vocabulary text-oriented n-grams implementation, training and decoding particularities, portability across language pairs and tasks, and main difficulties as revealed in error analyses.
The use of linguistic knowledge to improve word alignment quality is also studied. A cooccurrence-based one-to-one word alignment algorithm is extended with verb form classification with successful results. Additionally, we evaluate the impact in word alignment and translation quality of Part-Of-Speech, base form, verb form classification and stemming on state-of-art word alignment tools.
Furthermore, the thesis proposes a translation model tackling verb form generation through an additional verb instance model, reporting experiments in English-to-Spanish tasks. Disagreement is addressed via incorporating a target Part-Of-Speech language model. Finally, we study the impact of morphology derivation on Ngram-based SMT formulation, empirically evaluating the quality gain that is to be gained via morphology reduction. [Source: Author]
2001-2021 Universidad de Alicante DOI: 10.14198/bitra
Comentarios o sugerencias
La versión española de esta página es obra de Javier Franco
Nueva búsqueda
European Society for Translation Studies Ministerio de Educación Ivitra : Institut Virtual Internacional de Traducció asociación ibérica de estudios de traducción e interpretación