Chapter 12. Exploring variation in translation with probabilistic language models
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Alina Karakanta
Abstract
While some authors have suggested that translationese fingerprints are universal, others have shown that there is a fair amount of variation among translations due to source language shining through, translation type or translation mode. In our work, we attempt to gain empirical insights into variation in translation, focusing here on translation mode (translation vs. interpreting). Our goal is to discover features of translationese and interpretese that distinguish translated and interpreted output from comparable original text/speech as well as from each other at different linguistic levels. We use relative entropy (Kullback-Leibler Divergence) and visualization with word clouds. Our analysis shows differences in typical words between originals vs. non-originals as well as between translation modes both at lexical and grammatical levels.
Abstract
While some authors have suggested that translationese fingerprints are universal, others have shown that there is a fair amount of variation among translations due to source language shining through, translation type or translation mode. In our work, we attempt to gain empirical insights into variation in translation, focusing here on translation mode (translation vs. interpreting). Our goal is to discover features of translationese and interpretese that distinguish translated and interpreted output from comparable original text/speech as well as from each other at different linguistic levels. We use relative entropy (Kullback-Leibler Divergence) and visualization with word clouds. Our analysis shows differences in typical words between originals vs. non-originals as well as between translation modes both at lexical and grammatical levels.
Chapters in this book
- Prelim pages i
- Table of contents v
- Corpus resources and tools 1
-
Part I. Corpus resources and tools
- Chapter 1. Now what ? 23
- Chapter 2. ZHEN 49
- Chapter 3. Word alignment in a parallel corpus of Old English prose 75
- Chapter 4. Semantic textual similarity based on deep learning 101
- Chapter 5. TAligner 3.0 125
- Chapter 6. Developing a corpus-informed tool for Spanish professionals writing specialised texts in English 147
-
Part II. Corpus-based studies and explorations
- Chapter 7. English and Spanish discourse markers in translation 177
- Chapter 8. The discourse markers well and so and their equivalents in the Portuguese and Turkish subparts of the TED-MDB corpus 209
- Chapter 9. Variation of evidential values in discourse domains 233
- Chapter 10. The translation for dubbing of Westerns in Spain 257
- Chapter 11. Generic analysis of mobile application reviews in English and Spanish 283
- Chapter 12. Exploring variation in translation with probabilistic language models 307
- Chapter 13. Binomial adverbs in Germanic and Romance Languages 325
- Index 343
Chapters in this book
- Prelim pages i
- Table of contents v
- Corpus resources and tools 1
-
Part I. Corpus resources and tools
- Chapter 1. Now what ? 23
- Chapter 2. ZHEN 49
- Chapter 3. Word alignment in a parallel corpus of Old English prose 75
- Chapter 4. Semantic textual similarity based on deep learning 101
- Chapter 5. TAligner 3.0 125
- Chapter 6. Developing a corpus-informed tool for Spanish professionals writing specialised texts in English 147
-
Part II. Corpus-based studies and explorations
- Chapter 7. English and Spanish discourse markers in translation 177
- Chapter 8. The discourse markers well and so and their equivalents in the Portuguese and Turkish subparts of the TED-MDB corpus 209
- Chapter 9. Variation of evidential values in discourse domains 233
- Chapter 10. The translation for dubbing of Westerns in Spain 257
- Chapter 11. Generic analysis of mobile application reviews in English and Spanish 283
- Chapter 12. Exploring variation in translation with probabilistic language models 307
- Chapter 13. Binomial adverbs in Germanic and Romance Languages 325
- Index 343
