Some Translation Studies informed suggestions for further balancing methodologies for machine translation quality evaluation
Bibliographic Data
| ID | 22024830 |
|---|---|
| Authors | Ralph Krüger (0000-0002-1009-3365, TH Köln - University of Applied Sciences, corresponding author) |
| Year | 2022 |
| Volume | 11 |
| Issue | 2 |
| Pages | 213-233 |
| Publication date | 2022-11-10 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Translation Spaces (JOURNAL) |
| Journal identifiers | ISSN: 2211-3711 • E-ISSN: 2211-372X |
| Publisher | John Benjamins Publishing Company (PUBLISHER • NL) |
| DOI | 10.1075/ts.21026.kru |
| OpenAlex | W4220731031 |
| Language | EN |
| Citations received | 4 |
| References cited | 16 |
This article intends to contribute to the current debate on the quality of neural machine translation (NMT) vs. (professional) human translation quality, where recently claims concerning (super)human performance of NMT systems have emerged. The article will critically analyse some current machine translation (MT) quality evaluation methodologies employed in studies claiming such performance of their MT systems. This analysis aims to identify areas where these methodologies are potentially biased in favour of MT and hence may overvalue MT performance while undervaluing human translation performance. Then, the article provides some Translation Studies informed suggestions for improving or debiasing these methodologies in order to arrive at a more balanced picture of MT vs. (professional) human translation quality
Cognitive science · Debiasing · Epistemology · Machine translation · Management science · Computer Science · Engineering · Medicine · Natural Language Processing Techniques · Psychology · Software Engineering Research · Topic Modeling · Artificial Intelligence
| Citations per year | 1.33 |
|---|---|
| Citation span | 2023 - 2025 (3) |
| Highly cited | No |