Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation

Vivien Macketanz, Babak Naderi, Steven Schmidt, Sebastian Möller


Abstract
The quality of machine-generated text is a complex construct consisting of various aspects and dimensions. We present a study that aims to uncover relevant perceptual quality dimensions for one type of machine-generated text, that is, Machine Translation. We conducted a crowdsourcing survey in the style of a Semantic Differential to collect attribute ratings for German MT outputs. An Exploratory Factor Analysis revealed the underlying perceptual dimensions. As a result, we extracted four factors that operate as relevant dimensions for the Quality of Experience of MT outputs: precision, complexity, grammaticality, and transparency.
Anthology ID:
2022.humeval-1.3
Volume:
Proceedings of the 2nd Workshop on Human Evaluation of NLP Systems (HumEval)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Anya Belz, Maja Popović, Ehud Reiter, Anastasia Shimorina
Venue:
HumEval
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
24–31
Language:
URL:
https://aclanthology.org/2022.humeval-1.3
DOI:
10.18653/v1/2022.humeval-1.3
Bibkey:
Cite (ACL):
Vivien Macketanz, Babak Naderi, Steven Schmidt, and Sebastian Möller. 2022. Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation. In Proceedings of the 2nd Workshop on Human Evaluation of NLP Systems (HumEval), pages 24–31, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation (Macketanz et al., HumEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.humeval-1.3.pdf
Video:
 https://aclanthology.org/2022.humeval-1.3.mp4
Code
 dfki-nlp/textq