Attentive Interactive Convolutional Matching for Community Question Answering in Social Multimedia
文献类型:会议论文
作者 | Jun Hu1; Shengsheng Qian2; Quan Fang2; Changsheng Xu1,2,3 |
出版日期 | 2018-10 |
会议日期 | October 22-26, 2018 |
会议地点 | Seoul, Republic of Korea |
英文摘要 | Nowadays, community-based question answering (CQA) services have accumulated millions of users to share valuable knowledge. An essential function in CQA tasks is the accurate matching of answers w.r.t given questions. Existing methods usually ignore the redundant, heterogeneous, and multi-modal properties of CQA systems. In this paper, we propose a multi-modal attentive interactive convolutional matching method (MMAICM) to model the multi-modal content and social context jointly for questions and answers in a unified framework for CQA retrieval, which explores the redundant, heterogeneous, and multi-modal properties of CQA systems jointly. A well-designed attention mechanism is proposed to focus on useful word-pair interactions and neglect meaningless and noisy word-pair interactions. Moreover, a multi-modal interaction matrix method and a novel meta-path based network representation approach are proposed to consider the multi-modal content and social context, respectively. The attentive interactive convolutional matching network is proposed to infer the relevance between questions and answers, which can capture both the lexical and the sequential information of the contents. Experiment results on two real-world datasets demonstrate the superior performance of MMAICM compared with other state-of-the-art algorithms. |
语种 | 英语 |
源URL | [http://ir.ia.ac.cn/handle/173211/25825] |
专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
作者单位 | 1.Hefei University of Technology 2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 3.University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Jun Hu,Shengsheng Qian,Quan Fang,et al. Attentive Interactive Convolutional Matching for Community Question Answering in Social Multimedia[C]. 见:. Seoul, Republic of Korea. October 22-26, 2018. |
入库方式: OAI收割
来源:自动化研究所
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