A Fusion Pretrained Approach for Identifying the Cause of Sarcasm Remarks
文献类型：期刊论文
| 作者 | Li, Qiudan1 ; Xu, David Jingjun2; Qian, Haoda1,3; Wang, Linzi1,3; Yuan, Minjie1,3; Zeng, Daniel Dajun1,3
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| 刊名 | INFORMS JOURNAL ON COMPUTING
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| 出版日期 | 2024-06-13 |
| 页码 | 16 |
| 关键词 | sarcasm cause detection pretrained language model fusion-attention mechanism dynamic interactive semantics sarcasm spark |
| ISSN号 | 1091-9856 |
| DOI | 10.1287/ijoc.2022.0285 |
| 通讯作者 | Li, Qiudan(qiudan.li@ia.ac.cn) |
| 英文摘要 | Sarcastic remarks often appear in social media and e -commerce platforms to express almost exclusively negative emotions and opinions on certain instances, such as dissatisfaction with a purchased product or service. Thus, the detection of sarcasm allows merchants to timely resolve users' complaints. However, detecting sarcastic remarks is difficult because of its common form of using counterfactual statements. The few studies that are dedicated to detecting sarcasm largely ignore what sparks these sarcastic remarks, which could be because of an empty promise of a merchant's product description. This study formulates a novel problem of sarcasm cause detection that leverages domain information, dialogue context information, and sarcasm sentences by proposing a pretrained language model -based approach equipped with a novel hybrid multihead fusion -attention mechanism that combines self -attention, target -attention, and a feed -forward neural network. The domain information and the dialogue context information are then interactively fused to obtain the domain -specific dialogue context representation, and bidirectionally enhanced sarcasm -cause pair representations are generated for detecting sarcasm spark. Experimental results on real -world data sets demonstrate the efficacy of the proposed model. The findings of this study contribute to the literature on sarcasm cause detection and provide business value to relevant stakeholders and consumers. |
| WOS关键词 | SENTIMENT |
| 资助项目 | National Natural Science Foundation of China[72293575] ; National Natural Science Foundation of China[62071467] ; National Natural Science Foundation of China[62141608] ; Research Grant Council of the Hong Kong Spe-cial Administrative Region, China[11500322] ; Research Grant Council of the Hong Kong Spe-cial Administrative Region, China[11500421] |
| WOS研究方向 | Computer Science ; Operations Research & Management Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001250060900001 |
| 出版者 | INFORMS |
| 资助机构 | National Natural Science Foundation of China ; Research Grant Council of the Hong Kong Spe-cial Administrative Region, China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/59070]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
| 通讯作者 | Li, Qiudan |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.City Univ Hong Kong, Coll Business, Dept Informat Syst, Hong Kong, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Qiudan,Xu, David Jingjun,Qian, Haoda,et al. A Fusion Pretrained Approach for Identifying the Cause of Sarcasm Remarks[J]. INFORMS JOURNAL ON COMPUTING,2024:16. |
| APA | Li, Qiudan,Xu, David Jingjun,Qian, Haoda,Wang, Linzi,Yuan, Minjie,&Zeng, Daniel Dajun.(2024).A Fusion Pretrained Approach for Identifying the Cause of Sarcasm Remarks.INFORMS JOURNAL ON COMPUTING,16. |
| MLA | Li, Qiudan,et al."A Fusion Pretrained Approach for Identifying the Cause of Sarcasm Remarks".INFORMS JOURNAL ON COMPUTING (2024):16. |
入库方式： OAI收割
来源：自动化研究所
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