Affiliations:
School of Computer Science & School of Cyberspace Science, Xiangtan University, Hunan Province, China
Correspondence:
[*]
Corresponding author. Xin Liu, PhD., School of Computer Science & School of Cyberspace Science, Xiangtan University, Hunan Province, China. E-mail: [email protected].
Abstract: The short text matching models can be roughly divided into representation-based and interaction-based approaches. However, current representation-based text matching models often lack the ability to handle sentence pairs and typically only perform feature interactions at the network’s top layer, which can lead to a loss of semantic focus. The interactive text matching model has significant shortcomings in extracting differential information between sentences and may ignore global information. To address these issues, this article proposes a model structure that combines a dual-tower architecture with an interactive component, which compensates for their respective weaknesses in extracting sentence semantic information. Simultaneously, a method for integrating semantic information is proposed, enabling the model to capture both the interactive information between sentence pairs and the differential information between sentences, thereby addressing the issues with the aforementioned approaches. In the process of network training, a combination of cross-entropy and cosine similarity is used to calculate the model loss. The model is optimized to a stable state. Experiments on the commonly used datasets of QQP and MRPC validate the effectiveness of the proposed model, and its performance is stably improved.
Keywords: Short text matching, representational structure, interactive structure, BERT, multi-angle information
