Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 177 for “"machine translation"”.
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Multimodal machine translation
… there has been a lot of progress being made in machine translation through deep learning networks. But there is relatively lesser progress made in using images to catalyze the translation tasks. In this study, we explore various models to incorporate the image features in the machine translation …
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Robust Neural Machine Translation
<p>This thesis aims for general robust Neural Machine Translation (NMT) that is agnostic to the test domain. NMT has achieved high quality on benchmarks with closed datasets such as WMT and NIST but can fail when the translation input contains noise due to, for example, mismatched domains or …
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Towards Gender-Inclusive Machine Translation
Machine translation systems have become essential tools for cross-lingual communication, yet they systematically encode and perpetuate gender bias. When translating into grammatical gender languages such as Italian, Spanish, and German, these systems default to masculine forms for gender-ambiguous …
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Morphology, analogy and machine translation
… two or more languages in a "transfer-based" MachineTranslation System. As to the nature and content of these representations, manyalternative proposals, put forward in both theoretical and computational linguistic circles,are carefully considered and extensively discussed. In particular, I …
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Lexical syntax for statistical machine translation
Statistical Machine Translation (SMT) is by far the most dominant paradigm of Machine Translation. This can be justified by many reasons, such as accuracy, scalability, computational efficiency and fast adaptation to new languages and domains. However, current approaches of Phrase-based SMT lacks …
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Word confidence measures for machine translation
… the quality of automatically generated translation has significantly improved in recent years. However, the performance of machine translation systems is still not perfect. For human users dealing with these systems, it is desirable to obtain a reliable indication of possible errors in …
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Deep Syntax in Statistical Machine Translation
Statistical Machine Translation (SMT) via deep syntactic transfer employs a three-stage architecture, (i) parse source language (SL) input, (ii) transfer SL deep syntactic structure to the target language (TL), and (iii) generate a TL translation. The deep syntactic transfer architecture achieves a …
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Resourcing machine translation with parallel treebanks
… of syntax-based approaches to data-driven machine translation (MT) are clear: given the right model, a combination of hierarchical structure, constituent labels and morphological information can be exploited to produce more fluent, grammatical translation output. This has been demonstrated …
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Target-Dominant Chinese-English Machine Translation
… describes a target-dominant Chinese-English machine translation system, which can translate a given Chinese news sentence into English. We conjecture that we can improve the state of the art of MT using a TDMT approach. This system has participated in the NIST (National Institute of Standards …
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Domain adaptation for neural machine translation
… of deep learning techniques has allowed Neural Machine Translation (NMT) models to become extremely powerful, given sufficient training data and training time. However, such translation models struggle when translating text of a specific domain. A domain may consist of text on a well-defined …
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Data-driven machine translation for sign languages
… thesis explores the application of data-driven machine translation (MT) to sign languages (SLs). The provision of an SL MT system can facilitate communication between Deaf and hearing people by translating information into the native and preferred language of the individual. We begin with an …
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Robust machine translation for multi-domain tasks
… extend the phrase-based approach to statistical machine translation. Due to improved concepts and algorithms, the quality of the generated translation hypotheses has been significantly improved in recent years. Still, the translation quality leaves a lot to be desired when going beyond …
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Statistical machine translation with cascaded probabilistic transducers
Statistical machine translation is based on the idea to extract information from bilingual corpora, which can be used to generate new translations. The current work combines aspects from example-based machine translation and from grammar-based approaches, esp. bilingual regular grammars, to develop …
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Rich Features in Phrase-Based Machine Translation
Název práce: Bohaté rysy ve frázovém strojovém překladu Autor: Kamil Kos Katedra (ústav): Ústav formální a aplikované lingvistiky Vedoucí diplomové práce: RNDr. Ondřej Bojar, Ph.D. e-mail vedoucího: bojar@ufal.mff.cuni.cz Klíčová slova: strojový překlad, hodnocení kvality, kontextový model, …
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Automatic Error Correction of Machine Translation Output
Představujeme MLFix, systém pro automatickou statistickou post-editaci, který je duchovním následníkem pravidlového systému, Depfixu. Cílem této práce bylo prozkoumat možné postupy automatické identifikace nejčastějších morfologických chyb tvořených současnými systémy pro strojový překlad a …
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Speech Adaptation Modeling for Statistical Machine Translation
Spoken language translation (SLT) exists within one of the most challenging intersections of speech and natural language processing. While machine translation (MT) has demonstrated its effectiveness on the translation of textual data, the translation of spoken language remains a challenge, largely …
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TransBooster:black box optimisation of machine translation systems
Machine Translation (MT) systems tend to underperform when faced with long, linguistically complex sentences. Rule-based systems often trade a broad but shallow linguistic coverage for a deep, fine-grained analysis since hand-crafting rules based on detailed linguistic analyses is time-consuming, …
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