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Automatic classification of human translation and machine translation : a study from the perspective of lexical diversity

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Fu_2021_Automatic_classification_of_human_MOTRA_91_CCBY.pdf (135.7Kb)
Date
31/05/2021
Author
Fu, Yingxue
Nederhof, Mark Jan
Keywords
Q Science (General)
Artificial Intelligence
3rd-DAS
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Abstract
By using a trigram model and fine-tuning a pretrained BERT model for sequence classification, we show that machine translation and human translation can be classified with an accuracy above chance level, which suggests that machine translation and human translation are different in a systematic way. The classification accuracy of machine translation is much higher than of human translation. We show that this may be explained by the difference in lexical diversity between machine translation and human translation. If machine translation has independent patterns from human translation, automatic metrics which measure the deviation of machine translation from human translation may conflate difference with quality. Our experiment with two different types of automatic metrics shows correlation with the result of the classification task. Therefore, we suggest the difference in lexical diversity between machine translation and human translation be given more attention in machine translation evaluation.
Citation
Fu , Y & Nederhof , M J 2021 , Automatic classification of human translation and machine translation : a study from the perspective of lexical diversity . in Y Bizzoni , E Teich , C España-Bonet & J van Genabith (eds) , Proceedings for the First Workshop on Modelling Translation : Translatology in the Digital Age . NEALT Proceedings Series , Linkoping University Electronic Press , pp. 91–99 , Workshop on Modelling Translation , Online City , Iceland , 31/05/21 . < https://aclanthology.org/previews/ingest-nodalida/2021.motra-1.10/ >
 
workshop
 
Publication
Proceedings for the First Workshop on Modelling Translation
ISSN
1650-3686
Type
Conference item
Rights
Copyright © 2021 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/.
Collections
  • University of St Andrews Research
URL
https://aclanthology.org/previews/ingest-nodalida/2021.motra-1.10/
URI
http://hdl.handle.net/10023/23304

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