Sentence Alignment of Ancient and Modern Chinese Based on Reinforcement Learning
Supervised machine translation based on deep learning has achieved good results,which it requires a large amount of high-quality aligned parallel corpora for training.For the Chinese historical and modern translation sce-nario,the relative scarcity of high-quality parallel corpora highlights the significant research value and necessity of text alignment in this field.In traditional research on bilingual sentence alignment,conventional methods use features such as length,vocabulary,and co-occurring characters in bilingual texts to establish a comprehensive eval-uation criterion for measuring the similarity between two sentence pairs.Such methods have limited ability to match sentence semantics and perform poorly in the many-to-many alignment mode.This paper proposes using pre-trained language models with powerful semantic capabilities and reinforcement learning training objectives based on dynamic programming algorithms to integrate paragraph-level global.Experimental results demonstrate that the model trained using the method proposed in this paper outperforms the previously best-performing baseline models,with particularly significant improvements in handling many-to-many alignment patterns.
bilingual alignmentpre-trained language modelreinforcement learningdynamic programming
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