DeepReviewer: Collaborative Grammar and Innovation Neural Network for Automatic Paper Review

Youfang Leng, Li Yu, Jie Xiong

Abstract: Nowadays, there are more and more papers submitted to various periodicals and conferences. Typically, reviewers need to read through the paper and give a review comment and score to it based on somehow certain criterion. This review process is labor intensive and time-consuming. Recently, AI technology is widely used to alleviate human labor burden. Can machine learn from human to review papers automatically? In this paper, we propose a collaborative grammar and innovation model - DeepReviewer to achieve automatic paper review. This model learning the semantic, grammar and innovative features of an article by three main well-designed components simultaneously. Moreover, these three factors are integrated by an attention layer to get the final review score of the paper. We crawled paper review data from Openreview and built a real data set. Experimental results demonstrate that our model exceeds many baselines.