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首页> 外文期刊>ACM transactions on Asian and low-resource language information processing >Hybridization between Neural Computing and Nature-Inspired Algorithms for a Sentence Similarity Model Based on the Attention Mechanism
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Hybridization between Neural Computing and Nature-Inspired Algorithms for a Sentence Similarity Model Based on the Attention Mechanism

机译：基于注意机制的句子相似模型的神经计算与自然启发算法之间的杂交

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摘要

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%.
机译：句子相似性分析已应用于许多领域，例如机器翻译，问题应答系统和语音客户服务。作为自然语言处理的基本任务，句子相似性分析在许多领域起着重要作用。句子相似性分析的任务是通过多种功能建立句子相似性评分模型。在以前的工作中，研究人员提出了各种模型来处理句子相似度的计算。但这些模型不考虑句子对的关联信息，但只输入模型中的输入句子对。在本文中，我们提出了一个基于多重特征注意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。混合算法不仅根据多个特征解决决策问题，而且还提高了模型的性能。在模型中，我们使用注意机制提取句子功能并分配权重。然后，卷积神经网络用于减少矩阵的尺寸。在培训过程中，我们将萤火虫算法集成在神经网络中。实验结果表明，我们模型的准确性为74.21％。

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