论文标题
迈向细粒度的因果推理和质量保证
Towards Fine-grained Causal Reasoning and QA
论文作者
论文摘要
了解因果关系是NLP应用程序成功的关键，尤其是在高风险领域。因果关系以各种观点出现，例如启用并阻止了这些因素，尽管它们的重要性在文献中被忽略了。本文介绍了一种新型的细粒因果推理数据集，并介绍了NLP中的一系列新型预测任务，例如因果关系检测，事件因果关系提取和因果质量质量质量质量质量。我们的数据集包含25K引起事件对的人类注释和多句样本中的24K提问对，其中每个人都可以具有多个因果关系。通过广泛的实验和分析，我们表明，数据集中的复杂关系为所有三个任务的最新方法带来了独特的挑战，并突出了潜在的研究机会，尤其是在开发“因果关系”方法时。
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.
