2022/04/15 by Linyi Yang, Zhen Wang, Yang, Linyi +7 · 1 citation
Computer Science · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Computation and Language (cs.CL) #FOS: Computer and information sciences #Logic in Computer Science (cs.LO) #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2204.07408
openalex publication_date 2022/04/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Understanding causality is key to the success of NLP applications, especially in high-stakes domains. Causality comes in various perspectives such as enable and prevent that, despite their importance, have been largely ignored in the literature. This paper introduces a novel fine-grained causal reasoning dataset and presents a series of novel predictive tasks in NLP, such as causality detection, event causality extraction, and Causal QA. Our dataset contains human annotations of 25K cause-effect event pairs and 24K question-answering pairs within multi-sentence samples, where each can have multiple causal relationships. Through extensive experiments and analysis, we show that the complex relations in our dataset bring unique challenges to state-of-the-art methods across all three tasks and highlight potential research opportunities, especially in developing "causal-thinking" methods.