Affiliation:
1. Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China
2. Hebei Key Laboratory of Intelligent Information Perception and Processing, The 54th Research Institute of CETC, Shijiazhuang 050081, China
Abstract
Semantic matching research is the cornerstone of research in the fields of natural language similarity measurement and sensor ontology matching (OM). In the existing Chinese semantic matching methods, there are some shortcomings, such as the single dimension of semantic expression, the insufficient expression of context semantic relations, and the insufficient interaction of semantic information between different sentences. This paper proposes a Chinese semantic matching algorithm based on RoBERTa-wwm-ext with Siamese interaction and fine-tuning representation (RSIFR). The RSIFR model initializes the model with RoBERTa-wwm-ext as a vector of text. Firstly, a Siamese structure with embedded soft alignment attention mechanism and BiLSTM is constructed to realize the information interaction between two sentences. Secondly, LSTM-BiLSTM network structure is constructed to enhance the expression of semantic logic before and after sentences. Then, build a training model with fine-tuning mechanism. Fine tune the text’s eigenvector parameters through label supervision. Finally, the fusion vectors of the sentence pairs are inserted into the MLP network layer, resulting in semantic matching results. RSIFR model starts from a variety of dimensions, strengthens the expression ability of vectors to text semantic relations, deeply mines the semantic similarities and differences between different sentences, and generally improves the Chinese semantic matching performance. Experiments on the public dataset LCQMC show that our model outperforms existing Chinese semantic matching models.
Funder
Natural Science Foundation of Guangxi Province
Subject
Computer Networks and Communications,Computer Science Applications
Reference29 articles.
1. Cross-lingual text similarity exploiting neural machine translation models
2. Multi-turn inference matching network for natural language inference;C. Liu
3. Matching Sensor Ontologies With Multi-Context Similarity Measure and Parallel Compact Differential Evolution Algorithm
4. Short Text Similarity Computation Method Based on Feature Expansion and Siamese Network;X. Niu
5. Attention-based multi-level network for text matching with feature fusion;Y. Yang
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献