Semantic Representation Using Sub-Symbolic Knowledge in Commonsense Reasoning
TL;DR: This study examined the restrictions on the question-reasoning process of the pre-trained language model, and the need for models to use the logical structure of abstract meaning representations (AMRs), and demonstrated that the proposed method performed best when the AMR graph was extended with ConceptNet.
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Abstract: The commonsense question and answering (CSQA) system predicts the right answer based on a comprehensive understanding of the question. Previous research has developed models that use QA pairs, the corresponding evidence, or the knowledge graph as an input. Each method executes QA tasks with representations of pre-trained language models. However, the ability of the pre-trained language model to comprehend completely remains debatable. In this study, adversarial attack experiments were conducted on question-understanding. We examined the restrictions on the question-reasoning process of the pre-trained language model, and then demonstrated the need for models to use the logical structure of abstract meaning representations (AMRs). Additionally, the experimental results demonstrated that the method performed best when the AMR graph was extended with ConceptNet. With this extension, our proposed method outperformed the baseline in diverse commonsense-reasoning QA tasks.
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Overview of Knowledge Reasoning for Knowledge Graph
Xinliang Liu,Tingyu Mao,Yanyan Shi,Yanzhao Ren +3 more
TL;DR: This paper reviews knowledge graph reasoning methods, categorizing them into triplet reasoning, causal inference, temporal inference, and commonsense reasoning, and discusses the incorporation of background knowledge to improve reasoning mechanisms and address remaining challenges.
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