Internal-External Information Enhanced Causal Reasoning

Causal reasoning is vitally important for various natural language processing, which needs text semantic understanding and rich knowledge information reserve. Causal question-answering (CQA), one of the causal reasoning tasks, aims to choose either the cause or effect of a given story sentence. It requires both background causal knowledge and the ability to infer cause-effect relations. However, existing studies ignore the logical and commonsense relationship between the contexts, which limits the model capability. In this paper, we propose a novel model of Semantic Internal-External Enhancement (SIEE) by enhancing both the internal and external knowledge. The model employs Abstract Meaning Representation (AMR) to capture the core semantic information and explicit structures. In addition, we explore the commonsense knowledge behind the key information in the context to provide more clues for reasoning. Finally, we combine the above internal and external information by using a semantic aggregator to aggregate the semantic information of neighbors on the keyword nodes. Experimental studies show the competitive performance of our proposed model over the state-of-the-art published results on three CQA benchmarks, e-CARE, COPA and BCOPA.