Abstract
We present a first attempt at predicting the quality of translations produced by human, professional translators. We examine datasets annotated for quality at sentence- and word-level for four language pairs and provide experiments with prediction models for these datasets. We compare the performance of such models against that of models built from machine translations, highlighting a number of challenges in estimating quality and detecting errors in human translations.- Anthology ID:
- 2014.amta-researchers.22
- Volume:
- Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track
- Month:
- October 22-26
- Year:
- 2014
- Address:
- Vancouver, Canada
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 288–300
- Language:
- URL:
- https://aclanthology.org/2014.amta-researchers.22
- DOI:
- Cite (ACL):
- Lucia Specia and Kashif Shah. 2014. Predicting human translation quality. In Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track, pages 288–300, Vancouver, Canada. Association for Machine Translation in the Americas.
- Cite (Informal):
- Predicting human translation quality (Specia & Shah, AMTA 2014)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2014.amta-researchers.22.pdf