Human rating of predicted post-editing effort is a common activity and has been used to train confidence estimation models. However, the correlation between human ratings and actual post-editing effort is under-measured. Moreover, the impact of presenting effort indicators in a post-editing user interface on actual post-editing effort has hardly been researched. In this study, ratings of perceived post-editing effort are tested for correlations with actual temporal, technical and cognitive post-editing effort. In addition, the impact on post-editing effort of the presentation of post-editing effort indicators in the user interface is also tested. The language pair involved in this study is English-Brazilian Portuguese. Our findings, based on a small sample, suggest that there is little agreement between raters for predicted post-editing effort and that the correlations between actual post-editing effort and predicted effort are only moderate, and thus an inefficient basis for MT confidence estimation. Moreover, the presentation of post-editing effort indicators in the user interface appears not to impact on actual post-editing effort.
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Cognitive effort was also recorded for this cohort using eye tracking, but the data will be analysed at a later stage.
This is a fork of HandyCAT (Hokamp and Liu 2015).
Participant anonymity is a standard requirement of the university research ethics approval process.
Using the esurv.org platform.
Available from www.cs.umd.edu/~snover/tercom.
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This research is supported by Science Foundation Ireland (Grant 12/CE/I2267) as part of the CNGL (www.cngl.ie) at Dublin City University, Research Brazil Ireland, and by the FALCON Project (falcon-project.eu), funded by the European Commission through the Seventh Framework Programme (FP7) Grant Agreement No. 610879. The authors would like to place on record their thanks to Research Brazil Ireland, and to staff and students at the Faculdade de Letras at UFMG.
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Moorkens, J., O’Brien, S., da Silva, I.A.L. et al. Correlations of perceived post-editing effort with measurements of actual effort. Machine Translation 29, 267–284 (2015). https://doi.org/10.1007/s10590-015-9175-2
- Post-editing effort
- Confidence estimation
- Confidence indicators
- Machine translation user evaluation