文章基本信息

标题：Is Neural Machine Translation the New State of the Art?
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作者：Sheila Castilho ; Joss Moorkens ; Federico Gaspari 等
期刊名称：The Prague Bulletin of Mathematical Linguistics
印刷版ISSN：0032-6585
电子版ISSN：1804-0462
出版年度：2017
卷号：108
期号：1
页码：109-120
DOI：10.1515/pralin-2017-0013
语种：English
出版社：Walter de Gruyter GmbH
摘要：This paper discusses neural machine translation (NMT), a new paradigm in the MT field, comparing the quality of NMT systems with statistical MT by describing three studies using automatic and human evaluation methods. Automatic evaluation results presented for NMT are very promising, however human evaluations show mixed results. We report increases in fluency but inconsistent results for adequacy and post-editing effort. NMT undoubtedly represents a step forward for the MT field, but one that the community should be careful not to oversell.