A simple and efficient text matching model based on deep interaction. Issue 6 (November 2021)

• Record Type: Journal Article
• Authors: Yu, Chuanming; Xue, Haodong; Jiang, Yifan; An, Lu; Li, Gang
• Is Part Of: Information processing & management. Volume 58:Issue 6(2021)
• Publication Date: 2021-11
• Subjects: Text matching -- Deep learning -- Deep interaction -- Attention mechanism -- Neural network; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2021.102738
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: We propose a novel model, namely Deep Interaction Text Matching (DITM). The proposed model can well capture the interaction information. This approach outperforms most of the state-of-the-art methods on multiple tasks. The model is simple and effective, with a high generalization ability. The study is of great significance to promote the practice of text matching. Abstract: In recent years, text matching has gained increasing research focus and shown great improvements. However, due to the long-distance dependency and polysemy, existing text matching models cannot effectively capture the contextual and implicit semantic information of texts. Additionally, existing models are lack of generalization ability when applied to different scenarios. In this study, we propose a novel Deep Interactive Text Matching (DITM) model by integrating the encoder layer, the co-attention layer, and the fusion layer as an interaction module, based on a matching-aggregation framework. In particular, the interaction process is iterated multiple times to obtain the in-depth interaction information, and the relationship between the text pair is extracted through the multi-perspective pooling. We conduct extensive experiments on four text matching tasks, i.e., opinion retrieval, answer selection, paraphrase identification and natural language inference. Compared with the state-of-the-art text matching methods, the proposed model achieved the best results on most of the tasks, which proves that our model could effectively capture the interactive information between text pairs, and has a high generalization ability among different tasks. Further multi-lingual investigations show the similarities of the performance between English and Chinese, which suggest that our model could be ported to other languages. The research contributes a simple and efficient implementation of text matching in a situation where there is limited computing capacity, and sheds light on leveraging text matching models to facilitate a range of downstream tasks.