Pairwise Causality Structure: Towards Nested Causality Mining on Financial Statements

Abstract

Causality mining, which aims to find cause-effect relations in text, is an important yet challenging problem in natural language understanding. The extraction of causal relations is beneficial to practitioners in document-intensive industries. For instance, it enables investors and regulators in financial industries to quickly understand the correlation between events in financial statements. However, this problem is difficult since the expression of causality is diverse, and more importantly, nested. Specifically, causality often has a nested structure, where a pair of cause-effect can be the cause of another higher-level causality. Recent works deal with this problem by a bottom-up relation extraction solution, but it performs worse for relations on higher levels. In this study, we find that the nested causality structure can be transformed into a graph of pairwise causality between sentence segments. Then we propose a two-step solution: first, a segmenter disassembles a sentence into segments by detecting causality connectives; second, a relation classifier predicts whether a pair of segments has cause-effect relation or not. Two modules above are trained jointly in our proposed Causality Detection Network (CDNet). On a large dataset we collect, the precision of our model reaches 92.11% and the recall reaches 93.07% for this task. Compared with the existing state-of-the-art solution, the precision of our model is improved by 3.28% and 3.03% for recall. We also observe that the percentage of exactly correct sentences from prediction is 74.26% without post-processing, indicating the hardness of our problem and space for improvement.