Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning

no code implementations • COLING 2022 • Zhong Qian, Heng Zhang, Peifeng Li, Qiaoming Zhu, Guodong Zhou

Document-level Event Factuality Identification (DEFI) predicts the factuality of a specific event based on a document from which the event can be derived, which is a fundamental and crucial task in Natural Language Processing (NLP).

Data Augmentation Machine Reading Comprehension +5

Interpretable Rumor Detection in Microblogs by Attending to User Interactions

1 code implementation • 29 Jan 2020 • Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian, Jing Jiang

We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network.

Sentence

Document-Level Event Factuality Identification via Adversarial Neural Network

no code implementations • NAACL 2019 • Zhong Qian, Peifeng Li, Qiaoming Zhu, Guodong Zhou

Document-level event factuality identification is an important subtask in event factuality and is crucial for discourse understanding in Natural Language Processing (NLP).

Sentence

Speculation and Negation Scope Detection via Convolutional Neural Networks

no code implementations • EMNLP 2016 • Zhong Qian, Peifeng Li, Qiaoming Zhu, Guodong Zhou, Zhunchen Luo, Wei Luo

Negation