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        <title>Human Evaluation of Neural Machine Translation: The Case of Deep Learning</title>
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    <abstract>Recent advances in artificial neural networks now have a great impact on translation technology. A considerable achievement was reached in this field with the publication of L’Apprentissage Profond. This book, originally written in English (Deep Learning), was entirely machine-translated into French and post-edited by several experts. In this context, it appears essential to have a clear vision of the performance of MT tools. Providing an evaluation of NMT is precisely the aim of the present research paper. To accomplish this objective, a framework for error categorisation was built and a comparative analysis of the raw translation output and the post-edited version was performed with the purpose of identifying recurring patterns of errors. The findings showed that even though some grammatical errors were spotted, the output was generally correct from a linguistic point of view. The most recurring errors are linked to the specialised terminology employed in this book. Further errors include parts of text that were not translated as well as edits based on stylistic preferences. The major part of the output was not acceptable as such and required several edits per segment, but some sentences were of publishable quality and were therefore left untouched in the final version.</abstract>
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%0 Conference Proceedings
%T Human Evaluation of Neural Machine Translation: The Case of Deep Learning
%A Escribe, Marie
%S Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)
%D 2019
%8 September
%I Incoma Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F escribe-2019-human
%X Recent advances in artificial neural networks now have a great impact on translation technology. A considerable achievement was reached in this field with the publication of L’Apprentissage Profond. This book, originally written in English (Deep Learning), was entirely machine-translated into French and post-edited by several experts. In this context, it appears essential to have a clear vision of the performance of MT tools. Providing an evaluation of NMT is precisely the aim of the present research paper. To accomplish this objective, a framework for error categorisation was built and a comparative analysis of the raw translation output and the post-edited version was performed with the purpose of identifying recurring patterns of errors. The findings showed that even though some grammatical errors were spotted, the output was generally correct from a linguistic point of view. The most recurring errors are linked to the specialised terminology employed in this book. Further errors include parts of text that were not translated as well as edits based on stylistic preferences. The major part of the output was not acceptable as such and required several edits per segment, but some sentences were of publishable quality and were therefore left untouched in the final version.
%R 10.26615/issn.2683-0078.2019_005
%U https://aclanthology.org/W19-8705
%U https://doi.org/10.26615/issn.2683-0078.2019_005
%P 36-46

Markdown (Informal)

[Human Evaluation of Neural Machine Translation: The Case of Deep Learning](https://aclanthology.org/W19-8705) (Escribe, RANLP 2019)

• Human Evaluation of Neural Machine Translation: The Case of Deep Learning (Escribe, RANLP 2019)

ACL

• Marie Escribe. 2019. Human Evaluation of Neural Machine Translation: The Case of Deep Learning. In Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019), pages 36–46, Varna, Bulgaria. Incoma Ltd., Shoumen, Bulgaria.