A Semantic Context-Aware Automatic Quality Scoring Method for Machine Translation Based on Pretraining Language Model

Nowadays, machine translation has been a prevalent Internet application. But there still lacks mature intelligent algorithms to automatically evaluate quality of machine translation results. Considering the complexity inside machine intelligence-based semantic comprehension, we resort to pretraining language model (PLM) to deal with this challenge. Hence, this paper proposes a semantic context context-aware automatic quality scoring method for machine translation based on a specific PLM. The purpose of introducing the calculation of sentiment vectors in research is to consider emotional information in machine translation quality automatic scoring methods, in order to improve the accuracy and robustness of scoring. In particular, a novel PLM that combines multiple key features and tasks is established, which is utilized to make encoding towards largescale initial sentences and object sentences. It is finely tuned by integrating two typical pretraining structures. By applying the proposed PLM to complex semantic context and analysis tasks, we finally demonstrate its effectiveness through experiments on the News Crawl corpus and WMT dataset. The obtained results show that the proposal method has achieved significant improvements in various evaluation indicators, demonstrating its superiority in the quality evaluation of machine translation by perceiving semantic contexts. Through comparison experiments, efficiency of the proposal can be acknowledged.