Knowledge Commons of Institute of Automation,CAS
Seq2Set2Seq: A Two-stage Disentangled Method for Reply Keyword Generation in Social Media | |
Liu, Jie1,2![]() ![]() ![]() | |
发表期刊 | ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
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ISSN | 2375-4699 |
2024-03-01 | |
卷号 | 23期号:3页码:20 |
通讯作者 | Zhang, Qing(zqicl@ncut.edu.cn) |
摘要 | Social media produces large amounts of content every day. How to predict the potential influences of the contents from a social reply feedback perspective is a key issue that has not been explored. Thus, we propose a novel task named reply keyword prediction in social media, which aims to predict the keywords in the potential replies in as many aspects as possible. One prerequisite challenge is that the accessible social media datasets labeling such keywords remain absent. To solve this issue, we propose a new dataset,1 to study the reply keyword prediction in social media. This task could be seen as a single-turn dialogue keyword prediction for open-domain dialogue system. However, existing methods for dialogue keyword prediction cannot be adopted directly, which has two main drawbacks. First, they do not provide an explicit mechanism to model topic complementarity between keywords which is crucial in social media to controllably model all aspects of replies. Second, the collocations of keywords are not explicitly modeled, which also makes it less controllable to optimize for fine-grained prediction since the context information is much less than that in dialogue. To address these issues, we propose a two-stage disentangled framework, which can optimize the complementarity and collocation explicitly in a disentangled fashion. In the first stage, we use a sequence-to-set paradigm via multi-label prediction and determinantal point processes, to generate a set of keyword seeds satisfying the complementarity. In the second stage, we adopt a set-to-sequence paradigm via seq2seq model with the keyword seeds guidance from the set, to generate the more-fine-grained keywords with collocation. Experiments show that this method can generate not only a more diverse set of keywords but also more relevant and consistent keywords. Furthermore, the keywords obtained based on this method can achieve better reply generation results in the retrieval-based system than others. |
关键词 | Social media reply keyword prediction text generation multi-label classification determinantal point processes |
DOI | 10.1145/3644074 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2020AAA0109703] ; National Natural Science Foundation of China[62076167] ; Beijing Municipal Education Commission-Beijing Natural Fund Joint Funding Project[KZ201910028039] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Municipal Education Commission-Beijing Natural Fund Joint Funding Project |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:001208772400007 |
出版者 | ASSOC COMPUTING MACHINERY |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/56994 |
专题 | 复杂系统认知与决策实验室 |
通讯作者 | Zhang, Qing |
作者单位 | 1.North China Univ Technol, Sch Informat Sci, Beijing, Peoples R China 2.Capital Normal Univ, China Language Intelligence Res Ctr, Beijing, Peoples R China 3.Capital Normal Univ, Coll Informat Engn, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 5.Beijing Unisound Informat Technol Co Ltd, Beijing, Peoples R China 6.CNONIX Natl Standard Applicat & Promot Lab, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Liu, Jie,Li, Yaguang,He, Shizhu,et al. Seq2Set2Seq: A Two-stage Disentangled Method for Reply Keyword Generation in Social Media[J]. ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING,2024,23(3):20. |
APA | Liu, Jie.,Li, Yaguang.,He, Shizhu.,Wu, Shun.,Liu, Kang.,...&Zhang, Qing.(2024).Seq2Set2Seq: A Two-stage Disentangled Method for Reply Keyword Generation in Social Media.ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING,23(3),20. |
MLA | Liu, Jie,et al."Seq2Set2Seq: A Two-stage Disentangled Method for Reply Keyword Generation in Social Media".ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 23.3(2024):20. |
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