Research on Decoding Method for Multimodal Language Translation Model Based on Constrained Decoding and Minimum Bayes
A multi-modal language translation model based on Transformer was studied and constructed to address the issues of low accuracy and ambiguity in cross language translation models,and a new decoding method was proposed.The results showed that in the English Vietnamese dataset,the complete model had the highest substitute value for bilingual evaluation,at 28.51%.In the data-sets of Newstest2017,Newstest2018,and CWMT2018,the multimodal translation model proposed by the research institute has the highest bilingual evaluation substitute values,at 24.52%and 24.47%,respectively.And 24.48%.The model that uses bilingual e-valuation substitute utility function performs the best in bilingual evaluation substitute indicators.The research results show that the proposed translation model has good language translation performance and accuracy,can effectively improve the traditional Transform-er model,and has good performance in long sentence translation.Therefore,it has important practical application value and pros-pects.Research can provide certain technical support for English translation,promote cross-cultural communication,and deepen cross-border trade.