{"title":"Explanation Mining","authors":"Bhavya, Chengxiang Zhai","doi":"10.1145/3386527.3406738","DOIUrl":null,"url":null,"abstract":"Explanations are used to provide an understanding of a concept, procedure, or reasoning to others. Although explanations are present online ubiquitously within textbooks, discussion forums, and many more, there is no way to mine them automatically to assist learners in seeking an explanation. To address this problem, we propose the task of Explanation Mining. To mine explanations of educational concepts, we propose a baseline approach based on the Language Modeling approach of information retrieval. Preliminary results suggest that incorporating knowledge from a model trained on the ELI5 (Explain Like I'm Five) dataset in the form of a document prior helps increase the performance of a standard retrieval model. This is encouraging because our method requires minimal in-domain supervision, as a result, it can be deployed for multiple online courses. We also suggest some interesting future work in the computational analysis of explanations.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"32 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Seventh ACM Conference on Learning @ Scale","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3386527.3406738","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Explanations are used to provide an understanding of a concept, procedure, or reasoning to others. Although explanations are present online ubiquitously within textbooks, discussion forums, and many more, there is no way to mine them automatically to assist learners in seeking an explanation. To address this problem, we propose the task of Explanation Mining. To mine explanations of educational concepts, we propose a baseline approach based on the Language Modeling approach of information retrieval. Preliminary results suggest that incorporating knowledge from a model trained on the ELI5 (Explain Like I'm Five) dataset in the form of a document prior helps increase the performance of a standard retrieval model. This is encouraging because our method requires minimal in-domain supervision, as a result, it can be deployed for multiple online courses. We also suggest some interesting future work in the computational analysis of explanations.
解释用于向他人提供对概念、程序或推理的理解。尽管在线教科书、讨论论坛和其他很多地方都有解释,但没有办法自动挖掘它们来帮助学习者寻找解释。为了解决这个问题,我们提出了解释挖掘的任务。为了挖掘教育概念的解释,我们提出了一种基于信息检索的语言建模方法的基线方法。初步结果表明,将ELI5 (Explain Like I’m Five)数据集上训练的模型中的知识以文档的形式合并在一起,有助于提高标准检索模型的性能。这是令人鼓舞的,因为我们的方法需要最少的域内监督,因此,它可以部署到多个在线课程中。我们还建议在解释的计算分析方面进行一些有趣的未来工作。