TY - GEN
T1 - Scaling up online question answering via similar question retrieval
AU - Geigle, Chase
AU - Zhai, Chengxiang
N1 - Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2016/4/25
Y1 - 2016/4/25
N2 - Faced with growing class sizes and the dawn of the MOOC, educators are in need of tools to help them cope with the growing number of questions asked in large classes since manually answering all the questions in a timely manner is infeasible. In this paper, we propose to exploit historical question/answer data accumulated for the same or similar classes as a basis for automatically answering previously asked questions via the use of information retrieval techniques. We further propose to leverage resolved questions to create test collections for quantitative evaluation of a question retrieval algorithm without requiring additional human effort. Using this evaluation methodology, we study the effectiveness of state of the art retrieval techniques for this special retrieval task, and perform error analysis to inform future directions.
AB - Faced with growing class sizes and the dawn of the MOOC, educators are in need of tools to help them cope with the growing number of questions asked in large classes since manually answering all the questions in a timely manner is infeasible. In this paper, we propose to exploit historical question/answer data accumulated for the same or similar classes as a basis for automatically answering previously asked questions via the use of information retrieval techniques. We further propose to leverage resolved questions to create test collections for quantitative evaluation of a question retrieval algorithm without requiring additional human effort. Using this evaluation methodology, we study the effectiveness of state of the art retrieval techniques for this special retrieval task, and perform error analysis to inform future directions.
KW - Community question answering
KW - Information retrieval
UR - http://www.scopus.com/inward/record.url?scp=84969961888&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84969961888&partnerID=8YFLogxK
U2 - 10.1145/2876034.2893428
DO - 10.1145/2876034.2893428
M3 - Conference contribution
AN - SCOPUS:84969961888
T3 - L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale
SP - 257
EP - 260
BT - L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale
PB - Association for Computing Machinery
T2 - 3rd Annual ACM Conference on Learning at Scale, L@S 2016
Y2 - 25 April 2016 through 26 April 2016
ER -