Chapter 6. From process to product
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Lucas Nunes Vieira
Abstract
Post-editing of machine translation (MT) is now increasingly implemented in the human translation workflow after studies in both industry and academia have demonstrated the efficacy of this practice. Post-editing still involves open questions, however, such as how best to train post-editors and how to estimate the effort required by post-editing tasks. In attempting to address some of these questions, many previous studies investigate the post-editing process, but less research has focused on the post-edited product. This chapter examines the link between the process and product of post-editing by checking to see how post-editing effort data relates to the quality of post-edited texts, assessed in terms of fluency (linguistic quality) and adequacy (translation accuracy). A statistical analysis indicated that the association between editing operations and the fluency of post-edited texts is dependent on the quality of the raw MT output. Interestingly, a negative association was observed between the number of eye fixations on the text and the quality of the post-edited translations. The chapter shows empirical evidence supporting the distinction between the concepts of translation fluency and adequacy, and postulates that automatic processes play a central role in post-editing performance.
Abstract
Post-editing of machine translation (MT) is now increasingly implemented in the human translation workflow after studies in both industry and academia have demonstrated the efficacy of this practice. Post-editing still involves open questions, however, such as how best to train post-editors and how to estimate the effort required by post-editing tasks. In attempting to address some of these questions, many previous studies investigate the post-editing process, but less research has focused on the post-edited product. This chapter examines the link between the process and product of post-editing by checking to see how post-editing effort data relates to the quality of post-edited texts, assessed in terms of fluency (linguistic quality) and adequacy (translation accuracy). A statistical analysis indicated that the association between editing operations and the fluency of post-edited texts is dependent on the quality of the raw MT output. Interestingly, a negative association was observed between the number of eye fixations on the text and the quality of the post-edited translations. The chapter shows empirical evidence supporting the distinction between the concepts of translation fluency and adequacy, and postulates that automatic processes play a central role in post-editing performance.
Chapters in this book
- Prelim pages i
- Table of contents v
- Introduction 1
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Part I. Cognitive processes in reading during translation
- Chapter 1. Reading for translation 17
- Chapter 2. Four fundamental types of reading during translation 55
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Part II. Literality, directionality and intralingual translation processes
- Chapter 3. Measuring translation literality 81
- Chapter 4. Translation, post-editing and directionality 107
- Chapter 5. Intralingual and interlingual translation 135
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Part III. Computing and assessing translation effort, performance, and quality
- Chapter 6. From process to product 161
- Chapter 7. Quality is in the eyes of the reviewer 187
- Chapter 8. Translation technology and learner performance 207
- Notes on contributors 235
- Index 241
Chapters in this book
- Prelim pages i
- Table of contents v
- Introduction 1
-
Part I. Cognitive processes in reading during translation
- Chapter 1. Reading for translation 17
- Chapter 2. Four fundamental types of reading during translation 55
-
Part II. Literality, directionality and intralingual translation processes
- Chapter 3. Measuring translation literality 81
- Chapter 4. Translation, post-editing and directionality 107
- Chapter 5. Intralingual and interlingual translation 135
-
Part III. Computing and assessing translation effort, performance, and quality
- Chapter 6. From process to product 161
- Chapter 7. Quality is in the eyes of the reviewer 187
- Chapter 8. Translation technology and learner performance 207
- Notes on contributors 235
- Index 241
