An Intelligent Framework and Process Optimization Algorithm for Foreign Propaganda Machine Translation based on Multi-Cultural Information Collection and Clustering

Abstract

An intelligent framework and process optimization algorithm for foreign propaganda machine translation based on multi-cultural information collection and clustering is designed and implemented in the paper. This paper establishes a term translation model and a joint translation model. The term translation model binds the translation probability of a term to the topic distribution of the document in which it is located, so as to guide the selection of translations that match the topic during term translation. With these theoretical information, the novel machine translation model will be defined. Phrase-based statistical machine translation does not conduct the in-depth linguistic analysis of the core source language and target language, but divides the source language sentences to be translated into word strings. The proposed research article integrates the multi-cultural information collection and clustering model to design the intelligent framework and process optimization algorithm for foreign propaganda machine translation. Through the experiment, the performance is finally validated.