Abstract
Research findings on machine translati on are discussed in a new report. According to news reporting originating from S handong, People's Republic of China, by NewsRx correspondents, research stated, "With the acceleration of globalization, machine translation (MT) plays an incre asingly prominent role in cross-language communication." The news journalists obtained a quote from the research from Qilu Medical Univer sity: "However, how to evaluate the quality of machine translation, especially c onsidering the nuances in different semantic contexts, remains a challenge. This paper proposes an automatic scoring model for machine translation quality based on deep transfer learning, which aims to accurately perceive and evaluate the q uality of translated texts in different semantic contexts. Firstly, a pre-traine d deep neural network model is used to extract the semantic feature representati on of the sentences in the source language and the target language, so as to cap ture the semantic information of the sentences. Then, deep transfer learning is used to map the semantic features of source language and target language into th e shared feature space. By sharing feature space, an effective relationship is e stablished between the semantic representations of two languages, so as to achie ve cross-language quality evaluation. The experimental results show that the mod el has made significant progress in evaluating the quality of machine translatio n."
NETL
NSTL