Hongwei Wang


2022

pdf bib
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios
Xinya Du | Zixuan Zhang | Sha Li | Pengfei Yu | Hongwei Wang | Tuan Lai | Xudong Lin | Ziqi Wang | Iris Liu | Ben Zhou | Haoyang Wen | Manling Li | Darryl Hannan | Jie Lei | Hyounghun Kim | Rotem Dror | Haoyu Wang | Michael Regan | Qi Zeng | Qing Lyu | Charles Yu | Carl Edwards | Xiaomeng Jin | Yizhu Jiao | Ghazaleh Kazeminejad | Zhenhailong Wang | Chris Callison-Burch | Mohit Bansal | Carl Vondrick | Jiawei Han | Dan Roth | Shih-Fu Chang | Martha Palmer | Heng Ji
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations

We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios. The framework consists of two parts: (1) an open-domain end-to-end multimedia multilingual information extraction system with weak-supervision and zero-shot learningbased techniques. (2) schema matching and schema-guided event prediction based on our curated schema library. We build a demo website based on our dockerized system and schema library publicly available for installation (https://github.com/RESIN-KAIROS/RESIN-11). We also include a video demonstrating the system.

2021

pdf bib
EventKE: Event-Enhanced Knowledge Graph Embedding
Zixuan Zhang | Hongwei Wang | Han Zhao | Hanghang Tong | Heng Ji
Findings of the Association for Computational Linguistics: EMNLP 2021

Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities. In this paper, we emphasize the importance of incorporating events in KG representation learning, and propose an event-enhanced KG embedding model EventKE. Specifically, given the original KG, we first incorporate event nodes by building a heterogeneous network, where entity nodes and event nodes are distributed on the two sides of the network inter-connected by event argument links. We then use entity-entity relations from the original KG and event-event temporal links to inner-connect entity and event nodes respectively. We design a novel and effective attention-based message passing method, which is conducted on entity-entity, event-entity, and event-event relations to fuse the event information into KG embeddings. Experimental results on real-world datasets demonstrate that events can greatly improve the quality of the KG embeddings on multiple downstream tasks.