Research on cross modal entity information fusion in AI artificial intelligence translation
To further improve the effectiveness of English translation in multilingual scenarios,a multimodal neural machine translation model based on context gating mechanism is proposed using a cross modal entity fusion method.Among them,the transla-tion model based on the multimodal neural machine translation model is optimized by introducing context gating mechanism to further improve the translation quality of the model.The experimental results show that compared with the baseline model and other transla-tion models introduced,the model constructed by the research institute can perform better English translation.On the three evaluation indicators of BLEU,METR,and TER,it achieved 63.0,77.5,23.1,62.8,76.4,and 23.5 on the Multi30k-16 and Multi30k-17 datasets,respectively;Compared with the baseline model,the model constructed by the research institute has achieved significant improvements in both fidelity and fluency.In summary,the multimodal neural machine translation model based on context gating mechanism constructed by the research institute has good performance and can effectively improve translation quality.When applied to actual multilingual English translation scenarios,it can achieve good translation results and is highly feasible.
English translationentity fusionMNMT modelgate control mechanismattention mechanism
万方数据
维普
