Toward Fine-grained Causality Reasoning and CausalQA

Abstract

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 first-of-its-kind, fine-grained causal reasoning dataset that contains seven causal relations and defines a series of NLP tasks, from causality detection to event causality extraction and causal reasoning. Our dataset contains human annotations of 25K cause-effect event pairs and 24K question-answering pairs within multi-sentence samples, where each can contain 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.

Tasks

Reproductions