On Generality and Knowledge Transferability in Cross-Domain Duplicate Question Detection for Heterogeneous Community Question Answering
- Mohomed Shazan Mohomed Jabbar
- Luke Kumar, University of Alberta (Computing Science)
- Hamman Samuel
- Mi-Young Kim
- Sankalp Prabhakar
- Randy Goebel
- Osmar R. Zaiane, University of Alberta (Database)
Duplicate question detection is an ongoing challenge in community question answering because semantically equivalent questions can have significantly different words and structures. In addition, the identification of duplicate questions can reduce the resources required for retrieval, when the same questions are not repeated. This study compares the performance of deep neural networks and gradient tree boosting, and explores the possibility of domain adaptation with transfer learning to improve the under-performing target domains for the text-pair duplicates classification task, using three heterogeneous datasets: general-purpose Quora, technical Ask Ubuntu, and academic English Stack Exchange. Ultimately, our study exposes the alternative hypothesis that the meaning of a "duplicate" is not inherently general-purpose, but rather is dependent on the domain of learning, hence reducing the chance of transfer learning through adapting to the domain.
Citation
M. Jabbar, L. Kumar, H. Samuel, M. Kim, S. Prabhakar, R. Goebel, O. Zaiane. "On Generality and Knowledge Transferability in Cross-Domain Duplicate Question Detection for Heterogeneous Community Question Answering". November 2018.| Keywords: | |
| Category: | In Journal |
| Web Links: | Cornell University |
BibTeX
@article{Jabbar+al:18,
author = {Mohomed Shazan Mohomed Jabbar and Luke Kumar and Hamman Samuel and
Mi-Young Kim and Sankalp Prabhakar and Randy Goebel and Osmar R. Zaiane},
title = {On Generality and Knowledge Transferability in Cross-Domain
Duplicate Question Detection for Heterogeneous Community Question
Answering},
year = 2018,
}Last Updated: June 19, 2020Submitted by Sabina P
