Multi-task Peer-Review Score Prediction
Jiyi Li, Ayaka Sato, Kazuya Shimura, Fumiyo Fukumoto · 2020
Automatic prediction of 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 aspect.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 thus propose a multi-task shared structure encoding approach that 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 naïve multi-task methods.