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Problem Solving Activities in Post-Editing and Translation from Scratch a Multi-Method Study

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Problem Solving Activities in Post-Editing and Translation from Scratch a Multi-Method Study Problem solving activities in post-editing and translation from scratch A multi-method study Jean Nitzke language Translation and Multilingual Natural science press Language Processing 12 Translation and Multilingual Natural Language Processing Editors: Oliver Czulo (Universität Leipzig), Silvia Hansen-Schirra (Johannes Gutenberg-Universität Mainz), Reinhard Rapp (Johannes Gutenberg-Universität Mainz, Hochschule Magdeburg-Stendal) In this series: 1. Fantinuoli, Claudio & Federico Zanettin (eds.). New directions in corpus-based translation studies. 2. Hansen-Schirra, Silvia & Sambor Grucza (eds.). Eyetracking and Applied Linguistics. 3. Neumann, Stella, Oliver Čulo & Silvia Hansen-Schirra (eds.). Annotation, exploitation and evaluation of parallel corpora: TC3 I. 4. Czulo, Oliver & Silvia Hansen-Schirra (eds.). Crossroads between Contrastive Linguistics, Translation Studies and Machine Translation: TC3 II. 5. Rehm, Georg, Felix Sasaki, Daniel Stein & Andreas Witt (eds.). Language technologies for a multilingual Europe: TC3 III. 6. Menzel, Katrin, Ekaterina Lapshinova-Koltunski & Kerstin Anna Kunz (eds.). New perspectives on cohesion and coherence: Implications for translation. 7. Hansen-Schirra, Silvia, Oliver Czulo & Sascha Hofmann (eds). Empirical modelling of translation and interpreting. 8. Svoboda, Tomáš, Łucja Biel & Krzysztof Łoboda (eds.). Quality aspects in institutional translation. 9. Fox, Wendy. Can integrated titles improve the viewing experience? Investigating the impact of subtitling on the reception and enjoyment of film using eye tracking and questionnaire data. 10. Moran, Steven & Michael Cysouw. The Unicode cookbook for linguists: Managing writing systems using orthography profiles. 11. Fantinuoli, Claudio (ed.). Interpreting and technology. 12. Nitzke, Jean. Problem solving activities in post-editing and translation from scratch: A multi-method study. ISSN: 2364-8899 Problem solving activities in post-editing and translation from scratch A multi-method study Jean Nitzke language science press Nitzke, Jean. 2019. Problem solving activities in post-editing and translation from scratch: A multi-method study (Translation and Multilingual Natural Language Processing 12). Berlin: Language Science Press. This title can be downloaded at: http://langsci-press.org/catalog/book/196 © 2019, Jean Nitzke Published under the Creative Commons Attribution 4.0 Licence (CC BY 4.0): http://creativecommons.org/licenses/by/4.0/ ISBN: 978-3-96110-131-3 (Digital) 978-3-96110-132-0 (Hardcover) ISSN: 2364-8899 DOI:10.5281/zenodo.2546446 Source code available from www.github.com/langsci/196 Collaborative reading: paperhive.org/documents/remote?type=langsci&id=196 Cover and concept of design: Ulrike Harbort Typesetting: Sebastian Nordhoff, Felix Kopecky, Jean Nitzke Proofreading: Andreas Hölzl, Aniefon Daniel, Carla Parra, Caroline Rossi, Jeroen van de Weijer, Joseph T. Farquharson, Rosetta Berger, Umesh Patil, Yvonne Treis Fonts: Linux Libertine, Libertinus Math, Arimo, DejaVu Sans Mono Typesetting software:Ǝ X LATEX Language Science Press Unter den Linden 6 10099 Berlin, Germany langsci-press.org Storage and cataloguing done by FU Berlin Contents Acknowledgments v Abbreviations vii 1 Introduction 1 2 Machine translation 3 2.1 Machine translation development ................. 4 2.2 Machine translation approaches .................. 7 2.3 Machine translation applications .................. 10 3 Post-editing 13 3.1 The development of post-editing .................. 14 3.2 The influence of pre-editing and controlled language ...... 17 4 Dealing with post-editing and machine translation – five perspectives 21 4.1 Post-editing and machine translation in (theoretical) translation studies ................................ 21 4.2 Post-editing and machine translation in translation process re- search ................................. 26 4.3 Post-editing and machine translation applications in practice .. 34 4.3.1 Pan American Health Organization (PAHO) ....... 34 4.3.2 European Commission (EC) ................ 35 4.3.3 Ford ............................. 37 4.3.4 DARPA ........................... 38 4.4 Post-editing and machine translation in the professional transla- tion community ........................... 40 4.5 Post-editing training ........................ 45 5 Problem solving in psychology and translation studies 51 5.1 Defining the term problem and differentiating between problem solving and decision making .................... 53 5.2 Problem solving in psychology ................... 56 Contents 5.3 Problem solving in translation studies ............... 65 5.4 Modeling the concept of problem solving in translation studies by adding psychological approaches ................ 75 6 Research hypotheses 87 7 The data set 91 7.1 A short introduction to methods in translation process research 91 7.1.1 Think-aloud protocols ................... 92 7.1.2 Questionnaires ....................... 93 7.1.3 Keylogging ......................... 96 7.1.4 Eyetracking ......................... 97 7.1.5 Neuroscientific methods .................. 99 7.1.6 Data triangulation and choice of participants ...... 101 7.2 General information on the data set, post-editing guidelines, and setup of the experiment ....................... 102 7.3 Placing the research hypotheses and methods into the field of translation process research .................... 106 7.4 Previous research with the data set ................ 107 7.5 Session durations .......................... 112 7.6 Complexity levels of the texts ................... 114 7.7 General keystroke effort for modifications ............ 115 7.8 General analysis of errors in the final texts ............ 119 7.9 Criticism of the data set ....................... 123 8 The questionnaires 127 8.1 The questionnaire prior to the experiment ............ 127 8.2 The retrospective questionnaire .................. 137 8.3 Discussion .............................. 144 9 Lexical problem solving: Internet research 149 9.1 Lexical problem solving: Introduction ............... 149 9.2 Lexical problem solving: screen recording data .......... 156 9.2.1 Introduction of hypotheses for lexical problem solving (screen recording data) ................... 156 9.2.2 Number of research instances ............... 159 9.2.3 Research effort ....................... 165 9.2.4 Non-use of the Internet .................. 170 9.2.5 Research effort in relation to the complexity level .... 172 ii Contents 9.2.6 Types of websites consulted ................ 174 9.2.7 Time spent on research .................. 179 9.2.8 Research according to phases in translation process ... 182 9.2.9 Research ending in no obvious result ........... 183 9.2.10 Summary and conclusion – screen recording data .... 187 9.3 Lexical problem solving: Eyetracking data on most researched words/phrases ............................ 189 9.3.1 Mean values of eyetracking data ............. 192 9.3.2 Statistical tests for eyetracking data ............ 194 9.3.3 Further analysis – Misleading machine translation ... 197 9.3.4 Comparing most researched words to least-/no-research words ............................ 198 9.3.5 Status and experience ................... 199 9.3.6 Summary and conclusion – Keylogging and eyetracking data ............................. 201 9.4 Overall conclusions and final remarks ............... 202 10 Syntactic problem solving 205 10.1 Overview production and processing times ............ 207 10.2 Analysis of the influence of syntactic MT quality ......... 210 10.2.1 Analysis of production and processing data concerning the quality of the MT output ................ 210 10.2.2 Syntactic analysis on the sentence level excluding non- syntactic factors ...................... 221 10.3 Summary ............................... 225 11 Hidden problem indicators 229 11.1 Discussion of problem identifying parameters .......... 229 11.2 Problematic part-of-speech categories ............... 232 11.2.1 Indications in Munit .................... 232 11.2.2 Indications in InEff ..................... 236 11.2.3 Indications in HTra and HCross .............. 238 11.3 Influence of problem indicators on keylogging and eyetracking data .................................. 240 11.4 Mapping the parameter with the results of the analysis of the research behaviour ......................... 250 12 An approach to statistically modelling translation problems with the help of translation process data in R 253 iii Contents 13 Summary and discussion 259 14 Final remarks and future research 267 Appendix A: Analysis of Research Instances 269 Appendix B: Processing data for most researched words 271 Appendix C: Analysis of machine translation output 275 Appendix D: Part-of-speech categories, and their relation to different parameters 277 References 285 Index 301 Name index ................................. 301 Language index .............................. 305 Subject index ................................ 306 iv Acknowledgments Hold your breath and count to ten, And fall apart and start again, Hold your breath and count to ten, Start again, start again… Placebo – English Summer Rain Sag nicht alles so kompliziert Weil ich versteh das garantiert nicht Denk nicht alles so kompliziert Weil ich versteh, dass das nix wird Wanda – Wenn ich Zwanzig Bin Two songs and eight lines that came to my mind again and again while preparing this book, although (often) quoted out of context. I regularly remembered the Placebo song during statistical analyses – a completely new field for me at the beginning of this whole
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