YVONNE TSAI2021-08-122021-08-1220200907676Xhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097608226&doi=10.1080%2f0907676X.2020.1849325&partnerID=40&md5=645839a8a59e14f1a53e9a8352742dc5https://scholars.lib.ntu.edu.tw/handle/123456789/577585In recent years, artificial intelligence-based translation tools for patent documents have undergone rapid development. In 2016, the World Intellectual Property Organization and the European Patent Office successively launched neural machine translation technology. Increasing the volume of English translations in Taiwan’s Patent Search System is necessary to strengthen Taiwan’s competitive advantages and connectivity with the world. The high-quality bilingual parallel corpora had a decisive influence on the effectiveness of the translation model. Neural machine translation systems for patent texts are trained using human-translated patent texts; thus, exploring the specific pattern of patent translation is vital for improving the future development of neural machine translation for patent texts and providing insight regarding changes in the translations of patent texts over time. In this study, the translation-specific features and patterns of patent translation were identified from accumulated human translations of patent texts over a long-term period. Findings are presented regarding (1) features of English translations, namely of patent abstracts, and the frequency and patterns of simplification, explicitation, normalisation, and levelling-out; (2) specific patterns of translated patent texts and comparable nontranslated patent texts from different periods; and (3) linguistic features of English translations of patent abstracts of the same period. ? 2020 Informa UK Limited, trading as Taylor & Francis Group.journal article10.1080/0907676X.2020.18493252-s2.0-85097608226