Commonsense Knowledge-Augmented Pretrained Language Models for Causal Reasoning Classification

• URL: http://arxiv.org/abs/2112.08615v1
• Date: Thu, 16 Dec 2021 04:38:40 GMT
• Title: Commonsense Knowledge-Augmented Pretrained Language Models for Causal Reasoning Classification
• Authors: Pedram Hosseini, David A. Broniatowski, Mona Diab
• Abstract summary: We triples in ATOMIC2020, a wide coverage commonsense reasoning knowledge graph, to verbalize natural language text. We evaluate the resulting model on answering commonsense reasoning questions.
• Score: 9.313899406300644
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Commonsense knowledge can be leveraged for identifying causal relations in text. In this work, we verbalize triples in ATOMIC2020, a wide coverage commonsense reasoning knowledge graph, to natural language text and continually pretrain a BERT pretrained language model. We evaluate the resulting model on answering commonsense reasoning questions. Our results show that a continually pretrained language model augmented with commonsense reasoning knowledge outperforms our baseline on two commonsense causal reasoning benchmarks, COPA and BCOPA-CE, without additional improvement on the base model or using quality-enhanced data for fine-tuning.

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