@inproceedings{li-etal-2020-multi-task,
title = "Multi-task Peer-Review Score Prediction",
author = "Li, Jiyi and
Sato, Ayaka and
Shimura, Kazuya and
Fukumoto, Fumiyo",
booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.sdp-1.14",
doi = "10.18653/v1/2020.sdp-1.14",
pages = "121--126",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Multi-task Peer-Review Score Prediction
%A Li, Jiyi
%A Sato, Ayaka
%A Shimura, Kazuya
%A Fukumoto, Fumiyo
%S Proceedings of the First Workshop on Scholarly Document Processing
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-multi-task
%X 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.
%R 10.18653/v1/2020.sdp-1.14
%U https://aclanthology.org/2020.sdp-1.14
%U https://doi.org/10.18653/v1/2020.sdp-1.14
%P 121-126
Markdown (Informal)
[Multi-task Peer-Review Score Prediction](https://aclanthology.org/2020.sdp-1.14) (Li et al., sdp 2020)
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.