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        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
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        <name type="personal">
            <namePart type="given">Chengqing</namePart>
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    <abstract>This work proposes a new method for manual evaluation of Machine Translation (MT) output based on marking actual issues in the translated text. The novelty is that the evaluators are not assigning any scores, nor classifying errors, but marking all problematic parts (words, phrases, sentences) of the translation. The main advantage of this method is that the resulting annotations do not only provide overall scores by counting words with assigned tags, but can be further used for analysis of errors and challenging linguistic phenomena, as well as inter-annotator disagreements. Detailed analysis and understanding of actual problems are not enabled by typical manual evaluations where the annotators are asked to assign overall scores or to rank two or more translations. The proposed method is very general: it can be applied on any genre/domain and language pair, and it can be guided by various types of quality criteria. Also, it is not restricted to MT output, but can be used for other types of generated text.</abstract>
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%0 Conference Proceedings
%T Informative Manual Evaluation of Machine Translation Output
%A Popović, Maja
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F popovic-2020-informative
%X This work proposes a new method for manual evaluation of Machine Translation (MT) output based on marking actual issues in the translated text. The novelty is that the evaluators are not assigning any scores, nor classifying errors, but marking all problematic parts (words, phrases, sentences) of the translation. The main advantage of this method is that the resulting annotations do not only provide overall scores by counting words with assigned tags, but can be further used for analysis of errors and challenging linguistic phenomena, as well as inter-annotator disagreements. Detailed analysis and understanding of actual problems are not enabled by typical manual evaluations where the annotators are asked to assign overall scores or to rank two or more translations. The proposed method is very general: it can be applied on any genre/domain and language pair, and it can be guided by various types of quality criteria. Also, it is not restricted to MT output, but can be used for other types of generated text.
%R 10.18653/v1/2020.coling-main.444
%U https://aclanthology.org/2020.coling-main.444
%U https://doi.org/10.18653/v1/2020.coling-main.444
%P 5059-5069

Markdown (Informal)

[Informative Manual Evaluation of Machine Translation Output](https://aclanthology.org/2020.coling-main.444) (Popović, COLING 2020)

• Informative Manual Evaluation of Machine Translation Output (Popović, COLING 2020)

ACL

• Maja Popović. 2020. Informative Manual Evaluation of Machine Translation Output. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5059–5069, Barcelona, Spain (Online). International Committee on Computational Linguistics.