期刊名称：Tradumàtica: traducció i tecnologies de la informació i la comunicació
印刷版ISSN：1578-7559
出版年度：2021
期号：19
DOI：10.5565/rev/tradumatica.286
语种：Catalan
出版社：Tradumàtica: traducció i tecnologies de la informació i la comunicació
摘要：The recent improvements in neural MT (NMT) have driven a shift from statistical MT (SMT) to NMT. However, to assess the usefulness of MT models for post-editing (PE) and have a detailed insight of the output they produce, we need to analyse the most frequent errors and how they affect the task. We present a pilot study of a fine-grained analysis of MT errors based on post-editors corrections for an English to Spanish medical text translated with SMT and NMT. We use the MQM taxonomy to compare the two MT models and have a categorized classification of the errors produced. Even though results show a great variation among post-editors’ corrections, for this language combination fewer errors are corrected by post-editors in the NMT output. NMT also produces fewer accuracy errors and errors that are less critical.
