摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-关于机器翻译的详细数据已经呈现。根据NewsRx Jour Nalists在卡塔尔多哈的新闻报道，研究表明：“这项研究探讨了使用机器翻译(MT)进行跨语言作者归属和作者性别认同的可行性。”我们的新闻记者从哈马德·本·哈利法大学的研究中获得了一句话：“计算文体学实验是在使用谷歌的神经机器翻译成英语的希腊博客语料库上进行的。随机森林算法HM被用于作者和性别特征描述，在原文和译文中使用不同的特征组[作者的多级n-gram Profiles，定量语言学(QL)，跨语言单词嵌入(CLWE)]。”
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on machine translation h ave been presented. According to news reporting from Doha, Qatar, by NewsRx jour nalists, research stated, "This study explores the feasibility of cross-linguist ic authorship attribution and the author's gender identification using Machine T ranslation (MT)." Our news journalists obtained a quote from the research from Hamad Bin Khalifa U niversity: "Computational stylistics experiments were conducted on a Greek blog corpus translated into English using Google's Neural MT. A Random Forest algorit hm was employed for authorship and gender profiling, using different feature gro ups [Author's Multilevel N-gram Profiles, quantitative lingui stics (QL), and cross-lingual word embeddings (CLWE)] in both original and translated texts."
NETL
NSTL