论文标题
RECO：可靠的因果关系推理通过结构性因果复发神经网络
ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks
论文作者
论文摘要
因果链推理（CCR）是许多决策AI系统的重要能力，该系统要求该模型通过连接因果对建立可靠的因果链。但是，CCR遇到了两个主要的传递问题：阈值效应和场景漂移。换句话说，要剪接的因果对可能具有冲突的阈值边界或场景。为了解决这些问题，我们提出了一个新型可靠的因果链推理框架〜（RECO），该框架引入了外源变量，以代表因果链中每个因果对的阈值和场景因素，并估算了通过结构的Causal Causal Causal recrenturent Recrenter Neural网络跨外源变量的阈值和场景矛盾。实验表明，RECO在中文和英语CCR数据集上的表现优于一系列强大的基线。此外，通过注入可靠的因果链知识，伯特可以比通过其他类型的知识增强的BERT模型在四个下游因果关系的任务上取得更好的性能。
Causal chain reasoning (CCR) is an essential ability for many decision-making AI systems, which requires the model to build reliable causal chains by connecting causal pairs. However, CCR suffers from two main transitive problems: threshold effect and scene drift. In other words, the causal pairs to be spliced may have a conflicting threshold boundary or scenario. To address these issues, we propose a novel Reliable Causal chain reasoning framework~(ReCo), which introduces exogenous variables to represent the threshold and scene factors of each causal pair within the causal chain, and estimates the threshold and scene contradictions across exogenous variables via structural causal recurrent neural networks~(SRNN). Experiments show that ReCo outperforms a series of strong baselines on both Chinese and English CCR datasets. Moreover, by injecting reliable causal chain knowledge distilled by ReCo, BERT can achieve better performances on four downstream causal-related tasks than BERT models enhanced by other kinds of knowledge.
