most citedImproving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement

7 citations · 19 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CL20217 cited

Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement

Xinyu Zuo, Pengfei Cao, Yubo Chen +4

Current models for event causality identification (ECI) mainly adopt a supervised framework, which heavily rely on labeled data for training. Unfortunately, the scale of current an…

cs.CL20217 cited

LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification

Xinyu Zuo, Pengfei Cao, Yubo Chen +4

Modern models for event causality identification (ECI) are mainly based on supervised learning, which are prone to the data lacking problem. Unfortunately, the existing NLP-related…

cs.CL2020

Joint Entity and Relation Extraction with Set Prediction Networks

Dianbo Sui, Yubo Chen, Kang Liu +3

The joint entity and relation extraction task aims to extract all relational triples from a sentence. In essence, the relational triples contained in a sentence are unordered. Howe…

cs.CL2020

KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision

Xinyu Zuo, Yubo Chen, Kang Liu +1

Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora. However, hand-labeled training data is expensive to produc…

cs.CL20201 cited

Towards Causal Explanation Detection with Pyramid Salient-Aware Network

Xinyu Zuo, Yubo Chen, Kang Liu +1

Causal explanation analysis (CEA) can assist us to understand the reasons behind daily events, which has been found very helpful for understanding the coherence of messages. In thi…

cs.CL2020

Distantly Supervised Relation Extraction in Federated Settings

Dianbo Sui, Yubo Chen, Kang Liu +1

This paper investigates distantly supervised relation extraction in federated settings. Previous studies focus on distant supervision under the assumption of centralized training,…