2 citations · 5 across the 11 of their papers we have counts for
11 papers
What Would Happen Next? Predicting Consequences from An Event Causality Graph
Chuanhong Zhan, Wei Xiang, Chao Liang +1
Existing script event prediction task forcasts the subsequent event based on an event script chain. However, the evolution of historical events are more complicated in real world s…
Identifying while Learning for Document Event Causality Identification
Cheng Liu, Wei Xiang, Bang Wang
Event Causality Identification (ECI) aims to detect whether there exists a causal relation between two events in a document. Existing studies adopt a kind of identifying after lear…
Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection
Songtao Liu, Bang Wang, Wei Xiang +2
Multifaceted ideology detection (MID) aims to detect the ideological leanings of texts towards multiple facets. Previous studies on ideology detection mainly focus on one generic f…
In-context Contrastive Learning for Event Causality Identification
Chao Liang, Wei Xiang, Bang Wang
Event Causality Identification (ECI) aims at determining the existence of a causal relation between two events. Although recent prompt learning-based approaches have shown promisin…
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…
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…
End-to-end Hyperspectral Image Change Detection Network Based on Band Selection
Qingren Yao, Yuan Zhou, Chang Tang +1
For hyperspectral image change detection (HSI-CD), one key challenge is to reduce band redundancy, as only a few bands are crucial for change detection while other bands may be adv…
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.…
Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction
Lu Dai, Bang Wang, Wei Xiang +1
Recently, prompt-tuning has attracted growing interests in event argument extraction (EAE). However, the existing prompt-tuning methods have not achieved satisfactory performance d…
eX-ViT: A Novel eXplainable Vision Transformer for Weakly Supervised Semantic Segmentation
Lu Yu, Wei Xiang, Juan Fang +2
Recently vision transformer models have become prominent models for a range of vision tasks. These models, however, are usually opaque with weak feature interpretability. Moreover,…
A Survey of Implicit Discourse Relation Recognition
Wei Xiang, Bang Wang
A discourse containing one or more sentences describes daily issues and events for people to communicate their thoughts and opinions. As sentences are normally consist of multiple…