Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks

no code implementations • ACL 2021 • Pengfei Cao, Xinyu Zuo, Yubo Chen, Kang Liu, Jun Zhao, Yuguang Chen, Weihua Peng

Specifically, to make use of the descriptive knowledge, we devise a Descriptive Graph Induction module to obtain and encode the graph-structured descriptive knowledge.

Descriptive Event Causality Identification

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

no code implementations • Findings (ACL) 2021 • Xinyu Zuo, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Weihua Peng, Yuguang Chen

Current models for event causality identification (ECI) mainly adopt a supervised framework, which heavily rely on labeled data for training.

Event Causality Identification Representation Learning

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

no code implementations • ACL 2021 • Xinyu Zuo, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Weihua Peng, Yuguang Chen

On the other hand, our approach employs a dual mechanism, which is a learnable augmentation framework and can interactively adjust the generation process to generate task-related sentences.

Data Augmentation Event Causality Identification

Event Extraction as Multi-turn Question Answering

no code implementations • Findings of the Association for Computational Linguistics 2020 • Fayuan Li, Weihua Peng, Yuguang Chen, Quan Wang, Lu Pan, Yajuan Lyu, Yong Zhu

Most traditional approaches formulate this task as classification problems, with event types or argument roles taken as golden labels.

Event Extraction Question Answering +2