Multi-task Peer-Review Score Prediction

Jiyi Li, Ayaka Sato, Kazuya Shimura, Fumiyo Fukumoto


Abstract
Automatic prediction on the peer-review aspect scores of academic papers can be a useful assistant tool for both reviewers and authors. To handle the small size of published datasets on the target aspect of scores, we propose a multi-task approach to leverage additional information from other aspects of scores for improving the performance of the target. Because one of the problems of building multi-task models is how to select the proper resources of auxiliary tasks and how to select the proper shared structures. We propose a multi-task shared structure encoding approach which automatically selects good shared network structures as well as good auxiliary resources. The experiments based on peer-review datasets show that our approach is effective and has better performance on the target scores than the single-task method and naive multi-task methods.
Anthology ID:
2020.sdp-1.14
Volume:
Proceedings of the First Workshop on Scholarly Document Processing
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | sdp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
121–126
Language:
URL:
https://aclanthology.org/2020.sdp-1.14
DOI:
10.18653/v1/2020.sdp-1.14
Bibkey:
Cite (ACL):
Jiyi Li, Ayaka Sato, Kazuya Shimura, and Fumiyo Fukumoto. 2020. Multi-task Peer-Review Score Prediction. In Proceedings of the First Workshop on Scholarly Document Processing, pages 121–126, Online. Association for Computational Linguistics.
Cite (Informal):
Multi-task Peer-Review Score Prediction (Li et al., sdp 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/2020.sdp-1.14.pdf
Video:
 https://slideslive.com/38940727
Data
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