77 citations · 93 across the 8 of their papers we have counts for
8 papers
Adaptive Prompt Learning with Distilled Connective Knowledge for Implicit Discourse Relation Recognition
Bang Wang, Zhenglin Wang, Wei Xiang +1
Implicit discourse relation recognition (IDRR) aims at recognizing the discourse relation between two text segments without an explicit connective. Recently, the prompt learning ha…
Debiased Pairwise Learning from Positive-Unlabeled Implicit Feedback
Bin Liu, Qin Luo, Bang Wang
Learning contrastive representations from pairwise comparisons has achieved remarkable success in various fields, such as natural language processing, computer vision, and informat…
DAPrompt: Deterministic Assumption Prompt Learning for Event Causality Identification
Wei Xiang, Chuanhong Zhan, Bang Wang
Event Causality Identification (ECI) aims at determining whether there is a causal relation between two event mentions. Conventional prompt learning designs a prompt template to fi…
Reducing Popularity Bias in Recommender Systems through AUC-Optimal Negative Sampling
Bin Liu, Erjia Chen, Bang Wang
Popularity bias is a persistent issue associated with recommendation systems, posing challenges to both fairness and efficiency. Existing literature widely acknowledges that reduci…
TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition
Wei Xiang, Chao Liang, Bang Wang
Implicit Discourse Relation Recognition (IDRR) aims at classifying the relation sense between two arguments without an explicit connective. Recently, the ConnPrompt~\cite{Wei.X:et.…
Prompt Learning for News Recommendation
Zizhuo Zhang, Bang Wang
Some recent \textit{news recommendation} (NR) methods introduce a Pre-trained Language Model (PLM) to encode news representation by following the vanilla pre-train and fine-tune pa…