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

Zhiyi Luo, Yizhu Liu and Shuyun Luo ()
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Zhiyi Luo: School of Computer Science and Technology and the Key Laboratory of Intelligent Textile and Flexible Interconnection of Zhejiang Province, Zhejiang Sci-Tech University, Hangzhou 310018, China
Yizhu Liu: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Shuyun Luo: School of Computer Science and Technology and the Key Laboratory of Intelligent Textile and Flexible Interconnection of Zhejiang Province, Zhejiang Sci-Tech University, Hangzhou 310018, China

Mathematics, 2023, vol. 11, issue 23, 1-14

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.

Keywords: deep learning; sequence to sequence; convolutional networks; commonsense causal reasoning; fine-tuning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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