35 citations · 68 across the 37 of their papers we have counts for
6 papers · 1 filter
An Information Minimization Based Contrastive Learning Model for Unsupervised Sentence Embeddings Learning
Shaobin Chen, Jie Zhou, Yuling Sun +1
Unsupervised sentence embeddings learning has been recently dominated by contrastive learning methods (e.g., SimCSE), which keep positive pairs similar and push negative pairs apar…
A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck
Jie Zhou, Qi Zhang, Qin Chen +2
Event argument extraction (EAE) aims to extract arguments with given roles from texts, which have been widely studied in natural language processing. Most previous works have achie…
Causal Intervention Improves Implicit Sentiment Analysis
Siyin Wang, Jie Zhou, Changzhi Sun +4
Despite having achieved great success for sentiment analysis, existing neural models struggle with implicit sentiment analysis. This may be due to the fact that they may latch onto…
CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument Extraction
Jiaju Lin, Qin Chen, Jie Zhou +2
Implicit event argument extraction (EAE) aims to identify arguments that could scatter over the document. Most previous work focuses on learning the direct relations between argume…
A Knowledge-Enhanced Adversarial Model for Cross-lingual Structured Sentiment Analysis
Qi Zhang, Jie Zhou, Qin Chen +3
Structured sentiment analysis, which aims to extract the complex semantic structures such as holders, expressions, targets, and polarities, has obtained widespread attention from b…
Enhancing Event-Level Sentiment Analysis with Structured Arguments
Qi Zhang, Jie Zhou, Qin Chen +2
Previous studies about event-level sentiment analysis (SA) usually model the event as a topic, a category or target terms, while the structured arguments (e.g., subject, object, ti…