Open-Domain Event Graph Induction for Mitigating Framing Bias


Researchers have proposed various information extraction (IE) techniques to convert news articles into structured knowledge for news understanding. However, none of the existing methods have explicitly addressed the issue of framing bias that is inherent in news articles. We argue that studying and identifying framing bias is a crucial step towards trustworthy event understanding. We propose a novel task, neutral event graph induction, to address this problem. An event graph is a network of events and their temporal relations. Our task aims to induce such structural knowledge with minimal framing bias in an open domain. We propose a three-step framework to induce a neutral event graph from multiple input sources. The process starts by inducing an event graph from each input source, then merging them into one merged event graph, and lastly using a Graph Convolutional Network to remove event nodes with biased connotations. We demonstrate the effectiveness of our framework through the use of graph prediction metrics and bias-focused metrics.

Kaiqiang Song
Kaiqiang Song
Senior Research Scientist

Kaiqiang Song (宋凯强) is a Senior Research Scientist at Tencent AI Lab, Seattle, specializing in Natural Language Processing. His research focuses on advancing artificial intelligence through machine learning, NLP, and large language models. He is dedicated to optimizing AI model architectures for practical applications like text summarization and text generation, bridging the gap between foundational AI research and real-world impact.