CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算
Hierarchical Graph Semantic Pooling Network for Multi-modal Community Question Answer Matching
Jun Hu1; Shengsheng Qian2,3; Quan Fang2,3; Changsheng Xu1,2,3,4
2019-10
Conference NameACM international conference on Multimedia
Conference DateOctober 21 - 25, 2019
Conference PlaceNice, France
Abstract

Nowadays, community question answering (CQA) systems have attracted millions of users to share their valuable knowledge. Matching relevant answers for a specific question is a core function of CQA systems. Previous interaction-based matching approaches show promising performance in CQA systems. However, they typically suffer from two limitations: (1) They usually model content as word sequences, which ignores the semantics provided by nonconsecutive phrases, long-distance word dependency and visual information. (2) Word-level interactions focus on the distribution of similar words in terms of position, while being agnostic to the semantic-level interactions between questions and answers. To address these limitations, we propose a Hierarchical Graph Semantic Pooling Network (HGSPN) to model the hierarchical semantic-level interactions in a unified framework for multi-modal CQA matching. Instead of viewing text content as word sequences, we convert them into graphs, which can model non-consecutive phrases and long-distance word dependency for better obtaining the composition of semantics. In addition, visual content is also modeled into the graphs to provide complementary semantics. A well-designed stacked graph pooling network is proposed to capture the hierarchical semantic-level interactions between questions and answers based on these graphs. A novel convolutional matching network is designed to infer the matching score by integrating the hierarchical semantic-level interaction features. Experimental results on two real-world datasets demonstrate that our model outperforms the state-of-the-art CQA matching models.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25837
Collection模式识别国家重点实验室_多媒体计算
Affiliation1.Hefei University of Technology
2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
3.University of Chinese Academy of Sciences
4.Peng Cheng Laboratory
Recommended Citation
GB/T 7714
Jun Hu,Shengsheng Qian,Quan Fang,et al. Hierarchical Graph Semantic Pooling Network for Multi-modal Community Question Answer Matching[C],2019.
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