PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space

Omer Anjum, Hongyu Gong, Suma Bhat, Wen-Mei Hwu, JinJun Xiong


Abstract
Finding the right reviewers to assess the quality of conference submissions is a time consuming process for conference organizers. Given the importance of this step, various automated reviewer-paper matching solutions have been proposed to alleviate the burden. Prior approaches including bag-of-words model and probabilistic topic model are less effective to deal with the vocabulary mismatch and partial topic overlap between the submission and reviewer. Our approach, the common topic model, jointly models the topics common to the submission and the reviewer’s profile while relying on abstract topic vectors. Experiments and insightful evaluations on two datasets demonstrate that the proposed method achieves consistent improvements compared to the state-of-the-art.
Anthology ID:
D19-1049
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
518–528
Language:
URL:
https://aclanthology.org/D19-1049
DOI:
10.18653/v1/D19-1049
Bibkey:
Cite (ACL):
Omer Anjum, Hongyu Gong, Suma Bhat, Wen-Mei Hwu, and JinJun Xiong. 2019. PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 518–528, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space (Anjum et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1049.pdf
Data
Microsoft Academic Graph