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A relation-aware representation approach for the question matching system

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Abstract

Online question matching is the process of comparing user queries with system questions to find appropriate answers. This task has become increasingly important with the popularity of knowledge sharing social networks in product search and intelligent Q &A in customer service. Many previous studies have focused on designing complex semantic structures through the questions themselves. In fact, the online user’s queries accumulate a large number of similar sentences, which have been grouped by semantics in the retrieval system. However, how to use these sentences to enhance the understanding of system questions is rarely studied. In this paper, we propose a novel Relation-aware Semantic Enhancement Network (RSEN) model. Specifically, we leverage the labels of the history records to identify different semantically related sentences. Then, we construct an expanded relation network to integrate the representation of different semantic relations. Furthermore, we interact we integrate the features of the system question with the semantically related sentences to augment the semantic information. Finally, we evaluate our proposed RSEN on two publicly available datasets. The results demonstrate the effectiveness of our proposed RSEN method compared to the advanced baselines.

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Availability of data and materials

The two datasets used in this paper are public datasets. They are available from the following two URLs. The Quora dataset is available from this URL (https://www.kaggle.com/quora/question-pairs-dataset). And the BQ dataset is available from this URL (http://icrc.hitsz.edu.cn/Article/show/175.html).

Notes

  1. https://www.kaggle.com/quora/question-pairs-dataset

  2. http://icrc.hitsz.edu.cn/Article/show/175.html

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants (U20A20229), in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region of China (2022D01A227).

Funding

This work was supported by the National Natural Science Foundation of China under Grants (U20A20229), in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region of China (2022D01A227).

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Authors and Affiliations

  1. The Anhui Province Key Laboratory of Big Data Analysis and Application, State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, 230052, AnHui, China

    Yanmin Chen, Enhong Chen, Qi Liu & Ruijun Sun

  2. Xinjiang Normal University, Urumqi, 830054, Xinjiang, China

    Yanmin Chen

  3. Hefei University of Technology, Hefei, 230009, AnHui, China

    Kun Zhang

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  1. Yanmin Chen
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  2. Enhong Chen
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Contributions

Y.C designed the theoretical foundation and logical framework of the study, and was responsible for the experimental operations and data processing and analysis. She also conducted in-depth research on the experimental results and wrote the first draft of the paper. E.C was responsible for the development and implementation of the research methodology, and conducted in-depth theoretical discussions and practical analysis of the research questions. In addition, he carefully reviewed and revised the article. K.Z improved and deepened the theoretical foundation and logical framework of the article, and provided detailed guidance and suggestions on data presentation and processing. In addition, he conducted an in-depth study of the experimental results, and carefully reviewed and revised the whole article. Q.L refined and deepened the research methodology, and provided guidance on the theoretical foundation and logical framework of the study. In addition, he carefully reviewed and revised the full text. R.S. worked extensively on the data presentation and processing of the article, and conducted in-depth research and discussion of the experimental results. In addition, he meticulously revised and embellished the structure and language of the article.

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Correspondence to Enhong Chen.

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Chen, Y., Chen, E., Zhang, K. et al. A relation-aware representation approach for the question matching system. World Wide Web 27, 17 (2024). https://doi.org/10.1007/s11280-024-01255-6

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