@inproceedings{macketanz-etal-2022-perceptual,
title = "Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation",
author = {Macketanz, Vivien and
Naderi, Babak and
Schmidt, Steven and
M{\"o}ller, Sebastian},
editor = "Belz, Anya and
Popovi{\'c}, Maja and
Reiter, Ehud and
Shimorina, Anastasia",
booktitle = "Proceedings of the 2nd Workshop on Human Evaluation of NLP Systems (HumEval)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.humeval-1.3",
doi = "10.18653/v1/2022.humeval-1.3",
pages = "24--31",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation
%A Macketanz, Vivien
%A Naderi, Babak
%A Schmidt, Steven
%A Möller, Sebastian
%Y Belz, Anya
%Y Popović, Maja
%Y Reiter, Ehud
%Y Shimorina, Anastasia
%S Proceedings of the 2nd Workshop on Human Evaluation of NLP Systems (HumEval)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F macketanz-etal-2022-perceptual
%X 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.
%R 10.18653/v1/2022.humeval-1.3
%U https://aclanthology.org/2022.humeval-1.3
%U https://doi.org/10.18653/v1/2022.humeval-1.3
%P 24-31
Markdown (Informal)
[Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation](https://aclanthology.org/2022.humeval-1.3) (Macketanz et al., HumEval 2022)
ACL