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English synchronous real-time translation method based on reinforcement learning

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Abstract

For many years, machine translation has been one of the most important and challenging topics in the field of natural language processing. In this work, we discuss and implement the real-time synchronous translation method, and focus on the key technologies to be solved in the translation generation of real-time synchronous translation method. The optimal template selection and phrase translation are the key factors affecting template machine translation. We improve the selection of the optimal template by using the methods of text template direct matching and template selection. In addition, the sequence-to-sequence model based on Recurrent Neural Network (RNN) has achieved good results in the task of translation text generation, but most of these models have the problems of text repetition, and exposure deviation. Aiming at the repetition problems, we propose a hybrid attention composed of stored attention and decoded self-attention, which is overcome by storing historical attention. In order to solve the problem of exposure bias and correct the loss function, we design a new training method based on reinforcement learning. In the experiment, we test the model on the ChinaDaily dataset and take Recall-Oriented Understudy for Gisting Evaluation (ROUGE) as the evaluation index. The results show that mixed attention can greatly improve the repetition problem, the exposure deviation can be eliminated with reinforcement learning, and the integrated model surpasses the State-of-the-Art algorithms in the test set.

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The data used to support the findings of this study is available from the corresponding author upon the reasonable request.

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  1. Tongling University, No. 1335, Cuihu Four Road, Tongguan District, Tongling, 244061, China

    Xin Ke

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Ke, X. English synchronous real-time translation method based on reinforcement learning. Wireless Netw (2022). https://doi.org/10.1007/s11276-022-02910-4

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