Natural Language Processing with Commonsense Knowledge: A Survey
It addresses the problem of enabling NLP models to perform better reasoning for researchers and practitioners, but it is incremental as it synthesizes existing work.
This survey explores the integration of commonsense knowledge into natural language processing (NLP) to enhance human-like reasoning, reviewing knowledge bases, benchmarks, methodologies, and challenges in the field.
Commonsense knowledge is essential for advancing natural language processing (NLP) by enabling models to engage in human-like reasoning, which requires a deeper understanding of context and often involves making inferences based on implicit external knowledge. This paper explores the integration of commonsense knowledge into various NLP tasks. We begin by reviewing prominent commonsense knowledge bases and then discuss the benchmarks used to evaluate the commonsense reasoning capabilities of NLP models, particularly language models. Furthermore, we highlight key methodologies for incorporating commonsense knowledge and their applications across different NLP tasks. The paper also examines the challenges and emerging trends in enhancing NLP systems with commonsense reasoning. All literature referenced in this survey can be accessed via our GitHub repository: https://github.com/yuboxie/awesome-commonsense.