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dc.contributor.authorMohaghegh, Mahsa
dc.date.accessioned2013-08-01T23:22:19Z
dc.date.available2013-08-01T23:22:19Z
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/10179/4703
dc.description.abstractMachine translation (MT), as applied to natural language processing, has undergone substantial development over the past sixty years. While there are a number of different approaches to MT, there has been increasing interest in statistical machine translation (SMT) as the preferred approach to MT. Advances in computational power, together with the exploration of new methods and algorithms have enabled a general improvement in the output quality in a number of systems for various language pairs using this approach. However, there is a significant lack of research work in the area of English/Persian SMT, mainly due to the scarcity of data for this language pair, and the shortage of fundamental resources such as large-scale bilingual corpora. Several research studies have been published on work in the area of machine translation involving the Persian language; however, results producing fluent, usable output are rare. This thesis shows how SMT was implemented with this language pair for the first time, and how we created a cutting-edge hybrid SMT system capable of delivering high-quality translation output. We present the development of what is currently the largest English/Persian parallel corpus, constructed using a web crawler to source usable online data, together with the concatenation of existing parallel corpora. As yet another contribution of the research, we propose an improved hybrid corpus alignment method involving sentence length-based and word correspondence-based models to align words, phrases and sentences in the corpus. We also show the impact that the corpus domain can have on the translation output, and the necessity to consider domains of both bilingual and monolingual corpora where they are included in the training and language models. Two open-source toolkits, Moses and Joshua, were modified to work with the Persian language, and their behaviour and performance results were compared to determine which performed better when implemented with the Persian language. We present our work in designing, testing, and implementing a novel, three-level Transfer-based automatic post-editing (APE) component based on grammatical rules, which operates by analysing, parsing, and POS-tagging the output, and implements functions as transformers which perform corrections to the text, from lexical transformation to complex syntactical rearrangement. We show that rule-based approaches to the task of post-editing are superior to the commonly-used statistical models, since they incorporate linguistic knowledge, and are strong in terms of syntax, morphology, and structural semantics – qualities which are very desirable when performing grammatical correction and syntactical restructuring. We implement independent manual evaluation as well as standard automatic techniques, in order to assess more accurately the translation output. This evaluation shows that the use of the APE component is able to improve translation output significantly, that is, by at least 25%, resulting in high-quality translation output. Our system performs well by using a combination of the capabilities of two main MT approaches – SMT and RBMT – in different areas of the system as a whole. SMT provides the main system with consistent, mathematical-based translation, and the Transfer-based algorithm in the APE component operates with comprehensive linguistic rules in order to improve incorrect sentences, and fine-tune translation output. This results in a robust, state-of-the-art system, which noticeably exceeds other currently available solutions for this language pair.en
dc.language.isoenen
dc.publisherMassey Universityen_US
dc.rightsThe Authoren_US
dc.subjectEnglish languageen
dc.subjectPersian languageen
dc.subjectMachine translatingen
dc.titleEnglish-Persian phrase-based statistical machine translation : enhanced models, search and training : a thesis presented in fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Engineering at Massey University, Albany (Auckland), New Zealanden
dc.typeThesisen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorMassey Universityen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophy (Ph.D.)en


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