Abstract
Translation quality and efficiency are of great importance in the language services industry, which is why production duration and error counts are frequently investigated in Translation Process Research. However, a clear picture has not yet emerged as to how these two variables can be optimized or how they relate to one another. In the present study, data from multiple English-Japanese translation sessions is used to predict the number of errors per segment using source text and translator characteristics. An analysis utilizing zero-inflated generalized linear mixed effects models revealed that two source text characteristics (syntactic complexity and the proportion of long words) and three translator characteristics (years of experience, the time translators spent reading a source text before translating, and the time translators spent revising a translation) significantly influenced the number of errors. Furthermore, a lower proportion of long words per source text sentence and more training led to a significantly higher probability of error-free translation. Based on these results, combined with findings from a previous study on production duration, it is concluded that years of experience and the duration of the final revision phase are important factors that have a positive impact on translation efficiency and quality- Anthology ID:
- 2022.amta-wetpr.4
- Volume:
- Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Workshop 1: Empirical Translation Process Research)
- Month:
- September
- Year:
- 2022
- Address:
- Editors:
- Michael Carl, Masaru Yamada, Longui Zou
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 29–40
- Language:
- URL:
- https://aclanthology.org/2022.amta-wetpr.4
- DOI:
- Cite (ACL):
- Haruka Ogawa. 2022. Predicting the number of errors in human translation using source text and translator characteristics. In Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Workshop 1: Empirical Translation Process Research), pages 29–40, None. Association for Machine Translation in the Americas.
- Cite (Informal):
- Predicting the number of errors in human translation using source text and translator characteristics (Ogawa, AMTA 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.amta-wetpr.4.pdf