Departamento de
Traducción e Interpretación


Tema:   Automática.
Autor:   Gupta, Rohit
Año:   2016
Título:   Use of language technology to improve matching and retrieval in translation memory
Lugar:   Wolverhampton
Editorial/Revista:   University of Wolverhampton
Páginas:   184
Idioma:   Inglés.
Tipo:   Tesis.
Disponibilidad:   Acceso abierto.
Índice:   1. Improving matching and retrieval using paraphrases; 2. Advanced semantic matching for TM.
Resumen:   Current Translation Memory (TM) tools lack semantic knowledge while matching. Most TM tools compute similarity at the string level, which does not take into account semantic aspects in matching. Therefore, semantically similar segments, which differ on the surface form, are often not retrieved. In this thesis, we present five novel and efficient approaches to incorporate advanced semantic knowledge in translation memory matching and retrieval. Two efficient approaches which use a paraphrase database to improve translation memory matching and retrieval are presented. Both automatic and human evaluations are conducted. The results on both evaluations show that paraphrasing improves matching and retrieval. An approach based on manually designed features extracted using NLP systems and resources is presented, where a Support Vector Machine (SVM) regression model is trained, which calculates the similarity between two segments. The approach based on manually designed features did not retrieve better matches than simple edit-distance. Two approaches for retrieving segments from a TM using deep learning are investigated. The first one is based on Long Short Term Memory (LSTM) networks, while the other one is based on Tree Structured Long Short Term Memory (Tree-LSTM) networks. Eight different models using different datasets and settings are trained. The results are comparable to a baseline which uses simple edit-distance. [Source: Author]
Agradecimientos:   Record supplied by Departament de Traducció i Interpretació i Estudis de l'Àsia Oriental (Universitat Autònoma de Barcelona).
2001-2019 Universidad de Alicante DOI: 10.14198/bitra
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