2021/09/05 by Lingzhi Wang, Wang, Lingzhi, Xingshan Zeng +9
Computer Science · #Artificial intelligence #Ask price #Computation and Language (cs.CL) #Computer science #Context (archaeology) #Conversation #FOS: Computer and information sciences #Machine learning #Point (geometry) #Recommender Systems and Techniques #Sentiment Analysis and Opinion Mining #Social media #Task (project management) #Topic Modeling #World Wide Web #cs.CL
paper · pdf · doi:10.48550/arxiv.2109.02020
published in arXiv (Cornell University) (Cornell University) · Accepted by EMNLP 2021 Findings
arxiv created 2021/09/05 · openalex publication_date 2021/09/05 · arxiv updated 2021/09/07 · openalex created_date 2021/11/22 · openalex updated_date 2026/07/28
In recent years, world business in online discussions and opinion sharing on social media is booming. Re-entry prediction task is thus proposed to help people keep track of the discussions which they wish to continue. Nevertheless, existing works only focus on exploiting chatting history and context information, and ignore the potential useful learning signals underlying conversation data, such as conversation thread patterns and repeated engagement of target users, which help better understand the behavior of target users in conversations. In this paper, we propose three interesting and well-founded auxiliary tasks, namely, Spread Pattern, Repeated Target user, and Turn Authorship, as the self-supervised signals for re-entry prediction. These auxiliary tasks are trained together with the main task in a multi-task manner. Experimental results on two datasets newly collected from Twitter and Reddit show that our method outperforms the previous state-of-the-arts with fewer parameters and faster convergence. Extensive experiments and analysis show the effectiveness of our proposed models and also point out some key ideas in designing self-supervised tasks.