{"type":"ScholarlyArticle","title":"Keep Skills in Mind: Understanding and Implementing Skills in Commonsense Question Answering","authors":[{"name":"Meikai Bao","slug":"meikai-bao","given":"Meikai","family":"Bao","affiliation":"Anhui Province Key Laboratory of Big Data Analysis and Application , University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence"},{"name":"Qi Liu","slug":"qi-liu","given":"Qi","family":"Liu","affiliation":"Anhui Province Key Laboratory of Big Data Analysis and Application , University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence"},{"name":"Kai Zhang","slug":"kai-zhang","given":"Kai","family":"Zhang","affiliation":"Anhui Province Key Laboratory of Big Data Analysis and Application , University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence"},{"name":"Ye Liu","slug":"ye-liu","given":"Ye","family":"Liu","affiliation":"Anhui Province Key Laboratory of Big Data Analysis and Application , University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence"},{"name":"Linan Yue","slug":"linan-yue","given":"Linan","family":"Yue","affiliation":"Anhui Province Key Laboratory of Big Data Analysis and Application , University of Science and Technology of China; State Key Laboratory of Cognitive Intelligence"},{"name":"Longfei Li","slug":"longfei-li","given":"Longfei","family":"Li","affiliation":"Ant Financial Services Group"},{"name":"Jun Zhou","slug":"jun-zhou","given":"Jun","family":"Zhou","affiliation":"Ant Financial Services Group"}],"abstract":"Commonsense Question Answering (CQA) aims to answer questions that require human commonsense. Closed-book CQA, as one of the subtasks, requires the model to answer questions without retrieving external knowledge, which emphasizes the importance of the model's problem-solving ability. Most previous methods relied on large-scale pre-trained models to generate question-related knowledge while ignoring the crucial role of skills in the process of answering commonsense questions. Generally, skills refer to the learned ability in performing a specific task or activity, which are derived from knowledge and experience. In this paper, we introduce a new approach named Dynamic Skill-aware Commonsense Question Answering (DSCQA), which transcends the limitations of traditional methods by informing the model about the need for each skill in questions and utilizes skills as a critical driver in CQA process. To be specific, DSCQA first employs commonsense skill extraction module to generate various skill representations. Then, DSCQA utilizes dynamic skill module to generate dynamic skill representations. Finally, in perception and emphasis module, various skills and dynamic skill representations are used to help question-answering process. Experimental results on two publicly available CQA datasets show the effectiveness of our proposed model and the considerable impact of introducing skills.","doi":"10.24963/ijcai.2023/557","url":"https://ijcai.org/proceedings/2023/557","pdf":"https://ijcai.org/proceedings/2023/0557.pdf","markdown":"https://ijcai.org/proceedings/2023/557.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/557","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Natural Language Processing","track":"Main Track","number":557,"pages":{"first":5012,"last":5020},"keywords":["Knowledge Representation and Reasoning: KRR: Common-sense reasoning","Natural Language Processing: NLP: Question answering"],"videos":[],"references":[{"position":1,"raw":"[Brown et al., 2020] Tom Brown, Benjamin Mann, Nick Ry- der, Melanie Subbiah, Jared D Kaplan, Prafulla Dhari- wal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 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