Northeast Normal University, Changchun, 130024 Jilin, China.
Comput Math Methods Med. 2022 Jun 2;2022:7068406. doi: 10.1155/2022/7068406. eCollection 2022.
In recent years, neural network-based English-Chinese translation models have gradually supplanted traditional translation methods. The neural translation model primarily models the entire translation process using the "encoder-attention-decoder" structure. Simultaneously, grammar knowledge is essential for translation, as it aids in the grammatical representation of word sequences and reduces grammatical errors. The focus of this article is on two major studies on attention mechanisms and grammatical knowledge, which will be used to carry out the following two studies. Firstly, in view of the existing neural network structure to build translation model caused by long distance dependent on long-distance information lost in the delivery, leading to problems in terms of the translation effect which is not ideal, put forward a kind of embedded attention long short-term memory (LSTM) network translation model. Secondly, in view of the lack of grammatical prior knowledge in translation models, a method is proposed to integrate grammatical information into translation models as prior knowledge. Finally, the proposed model is simulated on the IWSLT2019 dataset. The results show that the proposed model has a better representation of source language context information than the existing translation model based on the standard LSTM model.
近年来，基于神经网络的英中翻译模型逐渐取代了传统的翻译方法。神经翻译模型主要使用“编码器-注意力-解码器”结构来对整个翻译过程进行建模。同时，语法知识对于翻译来说也是必不可少的，因为它有助于词序列的语法表示并减少语法错误。本文的重点是关于注意力机制和语法知识的两项主要研究，将用于开展以下两项研究。首先，针对现有神经网络结构构建的翻译模型在传递过程中由于长距离依赖导致长距离信息丢失，从而导致翻译效果不理想的问题，提出了一种嵌入式注意力长短时记忆（LSTM）网络翻译模型。其次，针对翻译模型中缺乏语法先验知识的问题，提出了一种将语法信息作为先验知识集成到翻译模型中的方法。最后，在 IWSLT2019 数据集上对提出的模型进行了模拟。结果表明，与基于标准 LSTM 模型的现有翻译模型相比，所提出的模型对源语言上下文信息具有更好的表示能力。
