TY - GEN
T1 - Explanation Mining
AU - Bhavya,
AU - Zhai, Cheng Xiang
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/8/12
Y1 - 2020/8/12
N2 - 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.
AB - 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.
KW - explanations
KW - language modeling for information retrieval
KW - prior probability
UR - http://www.scopus.com/inward/record.url?scp=85094900160&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85094900160&partnerID=8YFLogxK
U2 - 10.1145/3386527.3406738
DO - 10.1145/3386527.3406738
M3 - Conference contribution
AN - SCOPUS:85094900160
T3 - L@S 2020 - Proceedings of the 7th ACM Conference on Learning @ Scale
SP - 321
EP - 324
BT - L@S 2020 - Proceedings of the 7th ACM Conference on Learning @ Scale
PB - Association for Computing Machinery
T2 - 7th Annual ACM Conference on Learning at Scale, L@S 2020
Y2 - 12 August 2020 through 14 August 2020
ER -