Sentence similarity analysis has been applied in many fields, such as machine translation, the question answering system, and voice customer service. As a basic task of natural language processing, sentence similarity analysis plays an important role in many fields. The task of sentence similarity analysis is to establish a sentence similarity scoring model through multi-features. In previous work, researchers proposed a variety of models to deal with the calculation of sentence similarity. But these models do not consider the association information of sentence pairs, but only input sentence pairs into the model. In this article, we propose a sentence feature extraction model based on multi-feature attention. In addition, with the development of deep learning and the application of nature-inspired algorithms, researchers have proposed various hybrid algorithms that combine nature-inspired algorithms with neural networks. The hybrid algorithms not only solve the problem of decision-making based on multiple features but also improve the performance of the model. In the model, we use the attention mechanism to extract sentence features and assign weight. Then, the convolutional neural network is used to reduce the dimension of the matrix. In the training process, we integrate the firefly algorithm in the neural networks. The experimental results show that the accuracy of our model is 74.21%.

中文翻译：

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基于注意力机制的句子相似度模型的神经计算与自然启发算法的混合

句子相似度分析已应用于机器翻译、问答系统、语音客服等诸多领域。作为自然语言处理的一项基本任务，句子相似度分析在许多领域都发挥着重要作用。句子相似度分析的任务是通过多特征建立句子相似度评分模型。在之前的工作中，研究人员提出了多种模型来处理句子相似度的计算。但是这些模型没有考虑句子对的关联信息，而只是将句子对输入到模型中。在本文中，我们提出了一种基于多特征注意力的句子特征提取模型。此外，随着深度学习的发展和自然启发算法的应用，研究人员提出了各种混合算法，将自然启发算法与神经网络相结合。混合算法不仅解决了基于多个特征的决策问题，而且提高了模型的性能。在模型中，我们使用注意力机制来提取句子特征并分配权重。然后，使用卷积神经网络对矩阵进行降维。在训练过程中，我们将萤火虫算法集成到神经网络中。实验结果表明，我们模型的准确率为74.21%。我们使用注意力机制来提取句子特征并分配权重。然后，使用卷积神经网络对矩阵进行降维。在训练过程中，我们将萤火虫算法集成到神经网络中。实验结果表明，我们模型的准确率为74.21%。我们使用注意力机制来提取句子特征并分配权重。然后，使用卷积神经网络对矩阵进行降维。在训练过程中，我们将萤火虫算法集成到神经网络中。实验结果表明，我们模型的准确率为74.21%。