Cross-Linguistic Similarity Evaluation Techniques Based on Deep Learning

Abstract

For the cross-linguistic similarity problem, a twin network model with ordered neuron long- and short-term memory neural network as a subnet is proposed. The model fuses bilingual word embeddings and encodes the representation of input sequences by ordered neuron long- and short-term memory neural networks. Based on this, the distributed semantic vector representation of the sentences is jointly constructed by using the global modelling capability of the fully connected network for higher-order semantic extraction. The final output part is the similarity of the bilingual sentences and is optimized by optimizing the parameters of each layer in the framework. Multiple experiments on the dataset show that the model achieves 81.05% accuracy, which effectively improves the accuracy of text similarity and converges faster and improves the semantic text analysis to some extent.