Approches quantitatives de l'analyse des pr{é}dictions en traduction automatique neuronale (TAN)
Université de Paris（巴黎大学）
英文摘要：
As part of a larger project on optimal learning conditions in neural machine translation, we investigate characteristic training phases of translation engines. All our experiments are carried out using OpenNMT-Py: the pre-processing step is implemented using the Europarl training corpus and the INTERSECT corpus is used for validation. Longitudinal analyses of training phases suggest that the progression of translations is not always linear. Following the results of textometric explorations, we identify the importance of the phenomena related to chronological progression, in order to map different processes at work in neural machine translation (NMT).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
