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GIMM: A graph convolutional network-based paraphrase identification model to detecting duplicate questions in QA communities

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Abstract

Paraphrase Identification (PI) is an important task in Natural Language Processing (NLP), which aims to detect whether two sentences expressed in various forms are semantically consistent. It can be used to solve the problem of duplicate detection in QA Communities (eg: Quora and Stack Overflow). There have many studies that applied Convolutional Neural Networks to capture rich matching information between sentence pairs layer by layer. However, only a limited number of studies have explored the more flexible Graph Convolutional Networks (GCNs) for this task. GCN operates directly on the graph, and learns the representation of the node according to the neighborhood information of nodes. Thus, the interactive information between two sentences can be effectively integrated based on the local graph structure. In this paper, a Graph-based Interaction Matching model (GIMM) for PI is proposed. GIMM takes each word as a node, the word co-occurrence relations between sentence pairs, and the phrase relations within a single sentence as the relations between nodes to build the interaction graph. Then, the GCN are applied to learn the richer word representations based on the local structure of the graph. Finally, the node representations are aligned by the Attention mechanism to obtain the matching vector, and the results of PI are obtained by the Fully Connected Layer. We conduct experiments to compare the performance of GIMM with the current baselines on the Quora and Stack Overflow datasets. Experimental results demonstrate that the proposed model achieves excellent performance on both of these datasets.

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Data Availability

The data associated with our study can be made available upon request. Please contact the corresponding author Xuan Zhang (zhxuan@ynu.edu.cn).

Notes

  1. Our pre-trained word embeddings are openly available in https://nlp.stanford.edu/projects

  2. https://www.nltk.org/

  3. https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs

  4. https://anonymousaaai2019.github.io/

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Funding

This work was supported by the Science Foundation of Young and Middle-aged Academic and Technical Leaders of Yunnan under Grant No. 202205AC160040; Science Foundation of Yunnan Jinzhi Expert Workstation under Grant No. 202205AF150006; Major Project of Yunnan Natural Science Foundation under Grant No. 202302AE09002003; Science and Technology Project of Yunnan Power Grid Co., Ltd. under Grant No.YNKJXM20222254; the Postgraduate Research and Innovation Foundation of Yunnan University under Grant No. 2021Z112; Science Foundation of “Knowledge-driven intelligent software engineering innovation team”.

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Authors and Affiliations

  1. School of Software, Yunnan University, Kunming, Yunnan, China

    KunPeng Du, Xuan Zhang, Chen Gao, Rui Zhu, Qiong Nong & XianYu Yang

  2. School of Electro-Mechanical and Information, Yiwu Industrial & Commercial College, Yiwu, Zhejiang, China

    KunPeng Du

  3. Key Laboratory of Software Engineering of Yunnan Province, Kunming, Yunnan, China

    Xuan Zhang & Rui Zhu

  4. Engineering Research Center of Cyberspace, Kunming, Yunnan, China

    Xuan Zhang & Rui Zhu

  5. Electric Power Research Institute, Yunnan Power Grid Co., Ltd., Kunming, Yunnan, China

    ChunLin Yin

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  1. KunPeng Du
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Correspondence to Xuan Zhang.

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Du, K., Zhang, X., Gao, C. et al. GIMM: A graph convolutional network-based paraphrase identification model to detecting duplicate questions in QA communities. Multimed Tools Appl 83, 31251–31278 (2024). https://doi.org/10.1007/s11042-023-16592-3

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