Post Editing Of Machine Translation

Post-editing is possibly the oldest form of human-machine cooperation for translation. It has been a common practice for just about as long as operational machine translation systems have existed. Recently, however, there has been a surge of interest in post-editing among the wider user community, partly due to the increasing quality of machine translation output, but also to the availability of free, reliable software for both machine translation and post-editing. As a result, the practices and processes of the translation industry are changing in fundamental ways. This volume is a compilation of work by researchers, developers and practitioners of post-editing, presented at two recent events on post-editing: The first Workshop on Post-editing Technology and Practice, held in conjunction with the 10th Conference of the Association for Machine Translation in the Americas, held in San Diego, in 2012; and the International Workshop on Expertise in Translation and Post-editing Research and Application, held at the Copenhagen Business School, in 2012.

Artificial intelligence is changing and will continue to change the world we live in. These changes are also influencing the translation market. Machine translation (MT) systems automatically transfer one language to another within seconds. However, MT systems are very often still not capable of producing perfect translations. To achieve high quality translations, the MT output first has to be corrected by a professional translator. This procedure is called post-editing (PE). PE has become an established task on the professional translation market. The aim of this text book is to provide basic knowledge about the most relevant topics in professional PE. The text book comprises ten chapters on both theoretical and practical aspects including topics like MT approaches and development, guidelines, integration into CAT tools, risks in PE, data security, practical decisions in the PE process, competences for PE, and new job profiles.

Companies and organisations are increasingly using machine translation to improve efficiency and cost-effectiveness, and then edit the machine translated output to create a fluent text that adheres to given text conventions. This procedure is known as post-editing. Translation and post-editing can often be categorised as problem-solving activities. When the translation of a source text unit is not immediately obvious to the translator, or in other words, if there is a hurdle between the source item and the target item, the translation process can be considered problematic. Conversely, if there is no hurdle between the source and target texts, the translation process can be considered a task-solving activity and not a problem-solving activity. This study investigates whether machine translated output influences problem-solving effort in internet research, syntax, and other problem indicators and whether the effort can be linked to expertise. A total of 24 translators (twelve professionals and twelve semi-professionals) produced translations from scratch from English into German, and (monolingually) post-edited machine translation output for this study. The study is part of the CRITT TPR-DB database. The translation and (monolingual) post-editing sessions were recorded with an eye-tracker and a keylogging program. The participants were all given the same six texts (two texts per task). Different approaches were used to identify problematic translation units. First, internet research behaviour was considered as research is a distinct indicator of problematic translation units. Then, the focus was placed on syntactical structures in the MT output that do not adhere to the rules of the target language, as I assumed that they would cause problems in the (monolingual) post-editing tasks that would not occur in the translation from scratch task. Finally, problem indicators were identified via different parameters like Munit, which indicates how often the participants created and modified one translation unit, or the inefficiency (InEff) value of translation units, i.e. the number of produced and deleted tokens divided by the final length of the translation. Finally, the study highlights how these parameters can be used to identify problems in the translation process data using mere keylogging data.

This study challenges the idea that, given the effectiveness of machine translation, major costs could be reduced by using monolingual staff to post-edit translations. It presents studies of machine translation systems, and current research into translation process.

Translation Revision and Post-editing looks at the apparently dissolving boundary between correcting translations generated by human brains and those generated by machines. It presents new research on post-editing and revision in government and corporate translation departments, translation agencies, the literary publishing sector and the volunteer sector, as well as on training in both types of translation checking work. This collection includes empirical studies based on surveys, interviews and keystroke logging, as well as more theoretical contributions questioning such traditional distinctions as translating versus editing. The chapters discuss revision and post-editing involving eight languages: Afrikaans, Catalan, Dutch, English, Finnish, French, German and Spanish. Among the topics covered are translator/reviser relations and revising/post-editing by non-professionals. The book is key reading for researchers, instructors and advanced students in Translation Studies as well as for professional translators with a special interest in checking translations.

Translation Revision and Post-editing looks at the apparently dissolving boundary between correcting translations generated by human brains and those generated by machines. It presents new research on post-editing and revision in government and corporate translation departments, translation agencies, the literary publishing sector and the volunteer sector, as well as on training in both types of translation checking work. This collection includes empirical studies based on surveys, interviews and keystroke logging, as well as more theoretical contributions questioning such traditional distinctions as translating versus editing. The chapters discuss revision and post-editing involving eight languages: Afrikaans, Catalan, Dutch, English, Finnish, French, German and Spanish. Among the topics covered are translator/reviser relations and revising/post-editing by non-professionals. The book is key reading for researchers, instructors and advanced students in Translation Studies as well as for professional translators with a special interest in checking translations.

Designed for translators and other professional linguists, this work attempts to clarify, explain and exemplify the impact that computers have had and are having on their profession. The book concerns machine translation, computer-aided translation and the future of translation and the computer.