Departamento de
Traducción e Interpretación


Tema:   Automática.
Autor:   Denkowski, Michael
Año:   2015
Título:   Machine Translation for Human Translators
Lugar:   Pittsburgh (Pennsylvania)
Editorial/Revista:   Carnegie Mellon University
Páginas:   97
Idioma:   Inglés.
Tipo:   Tesis.
Disponibilidad:   Acceso abierto.
Resumen:   While machine translation is sometimes sufficient for conveying information across language barriers, many scenarios still require precise human-quality translation that MT is currently unable to deliver. Governments and international organizations such as the United Nations require accurate translations of content dealing with complex geopolitical issues. Community-driven projects such as Wikipedia rely on volunteer translators to bring accurate information to diverse language communities. As the amount of data requiring translation has continued to increase, the idea of using machine translation to improve the speed of human translation has gained significant traction. In the frequently employed practice of post-editing, a MT system outputs an initial translation and a human translator edits it for correctness, ideally saving time over translating from scratch. While general improvements in MT quality have led to productivity gains with this technique, the idea of designing translation systems specifically for post-editing has only recently caught on in research and commercial communities. In this work, we present extensions to key components of statistical machine translation systems aimed directly at reducing the amount of work required from human translators. We cast MT for post-editing as an online learning task where new training instances are created as humans edit system output and introduce an adaptive MT system that immediately learns from this human feedback. New translation rules are learned from the data and both feature scores and weights are updated after each sentence is post-edited. An extended feature set allows making fine-grained distinctions between background and post -editing data on a per-translation basis. We describe a simulated post-editing paradigm wherein existing reference translations are used as a stand-in for human editing during system tuning, allowing our adaptive systems to be built and deployed without any seed post-editing data. [Source: Author]
2001-2019 Universidad de Alicante DOI: 10.14198/bitra
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