English Semantic Recognition Based on Intelligent Algorithm
Abstract
In the process of translation, semantic barriers have attracted extensive attention from researchers. Taking the translation between Chinese and English as an example, this paper used intelligent algorithms to recognize the semantic role of English, introduced the semantic role labeling, designed a semantic role encoder, integrated the encoder with the transformer model, and tested the translation performance of the system. The experimental results showed that the BLEU-4 score of the combined system was significantly higher than the baseline system and the traditional transformer system. The average BLEU-4 values of the three systems were 35.02, 35.78, and 36.9, respectively, and the score of the combined system was the highest. The specific analysis of several examples also found that the translation results of the combined system were more reliable. The experimental results verify the effectiveness of the combined system in machine translation and the importance of semantic recognition in translation.References
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https://doi.org/10.31449/inf.v45i6.3728Downloads
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