Affiliation:
1. School of Computer Science & School of Cyberspace Science, Xiangtan University, Hunan Province, China
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.
Reference23 articles.
1. A proposed conceptual framework for a representational approach to information retrieval;Lin;SIGIR Forum,2021
2. Automatic question-answer pairs generation and question similarity mechanism in question answering system,;Aithal;Applied Intelligence,2021
3. Co-gat: A co-interactive graph attention network for joint dialog act recognition and sentiment classification in:;Qin;Proceedings of the AAAI Conference on Artificial Intelligence,2021
4. An efficient automated answer scoring system for punjabi language,;Walia;Egyptian Informatics Journala,2019
5. Looking beyond sentence-level natural language inference for question answering and text summarization in:;Mishra;Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,2021