Analysis of English Machine Translation Standards of Professional Terms under Big Data Context Technology

Abstract

In recent years, with the development of science and technology, information technology has also been widely used in various industries. In today's era of big data, great changes have taken place in translation, from the traditional manual translation at the beginning to the current machine translation. The advantage of machine translation lies in its fast translation speed and low cost, but certain errors will also occur in the translated translation. The translation errors of professional terms account for a larger proportion, which greatly affects the quality of the translation. At the same time, professional terminology is the core knowledge in the article, which will directly affect people's understanding of technical information in the professional field. The purpose of this article is to study the English machine translation standards of professional terms in the context of big data. This article focuses on the English machine translation of professional terms, and analyzes the English machine translation standards of professional terms in the context of big data based on relevant research at home and abroad. This article will analyze its professional terminology translation standards from all aspects, summarize and summarize the translation standards studied by various scholars, analyze the factors that affect translation standards, and discover various characteristics of translation standards. At the same time, in order to solve the shortcomings of machine translation in the translation of professional terminology in the past, this article attempts to summarize a set of objective and comprehensive English machine translation system evaluation standards for professional terminology, so as to improve its use efficiency. The experimental results show that from the ranking results, the highest comprehensive evaluation score is candidate translation 1, and the weight is as high as 27%. In the analysis of the translation results, information characteristics are added to reorder the candidate translations, and the most suitable context and the best translation are selected.