Exploration on Advanced Intelligent Algorithms of Artificial Intelligence for Verb Recognition in Machine Translation

Abstract

This article aimed to address the problems of word order confusion, context dependency, and ambiguity in traditional machine translation (MT) methods for verb recognition. By applying advanced intelligent algorithms of artificial intelligence, verb recognition can be better processed and the quality and accuracy of MT can be improved. Based on Neural machine translation (NMT), basic attention mechanisms, historical attention information, dynamically obtain information related to the generated words, and constraint mechanisms were introduced to embed semantic information, represent polysemy, and annotate semantic roles of verbs. This article used the Workshop on MT (WMT), British National Corpus (BNC), Gutenberg, Reuters Corpus, and OpenSubtitles corpus, and enhanced the data in the corpora. The improved NMT model was compared with traditional NMT models, Rule-Based MT (RBMT), and Statistical MT (SMT). The experimental results showed that the average verb semantic matching degree of the improved NMT model in five corpora was 0.85, and the average Bilingual Evaluation Understudy (BLEU) score in five corpora was 0.90. The improved NMT model in this article can effectively improve the accuracy of verb recognition in MT, providing new methods for verb recognition in MT.