Abstract
Fresh data on Machine Translation are presented in a new report. According to news originating from Himachal Prades, India, by NewsRx correspondents, research stated, “The fast advancement in machine translation models necessitates the development of accurate evaluation metrics that would allow researchers to track the progress in text languages. The evaluation of machine translation models is crucial since its results are exploited for improvements of translation models.” Our news journalists obtained a quote from the research from the National Institute of Technology, “However fully automatically evaluating the machine translation models in itself is a huge challenge for the researchers as human evaluation is very expensive, time-consuming, unreproducible. This paper presents a detailed classification and comprehensive survey on various fully automated evaluation metrics, which are used to assess the performance or quality of machine translated output. Various fully automatic evaluation metrics are classified into five categories that are lexical, character, semantic, syntactic, and semantic & syntactic evaluation metrics for better understanding purpose. Taking account of the challenges posed in the field of machine translation evaluation by Statistical Machine Translation and Neural Machine 42 Translation, along with a discussion on the advantages, disadvantages, and gaps for each fully automatic machine translation evaluation metric has been provided.”
NETL
NSTL
Springer Nature