A Convolutional Sequence-to-Sequence Attention Fusion Framework for Commonsense Causal Reasoning

Abstract

Commonsense causal reasoning is the process of understanding the causal dependency between common events or actions. Traditionally, it was framed as a selection problem. However, we cannot obtain enough candidates and need more flexible causes (or effects) in many scenarios, such as causal-based QA problems. Thus, the ability to generate causes (or effects) is an important problem. In this paper, we propose a causal attention mechanism that leverages external knowledge from CausalNet, followed by a novel fusion mechanism that combines global causal dependency guidance from the causal attention with local causal dependency obtained through multi-layer soft attention within the CNN seq2seq architecture. Experimental results consistently demonstrate the superiority of the proposed framework, achieving BLEU-1 scores of 20.06 and 36.94, BLEU-2 scores of 9.98 and 27.78, and human-evaluated accuracy rates of 35% and 52% for two evaluation datasets, outperforming all other baselines across all metrics on both evaluation datasets.