ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis

To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison. A good review needs to be knowledgeable, namely that the comments should be constructive and informative to help improve the paper; and explainable by providing detailed evidence. ReviewRobot achieves these goals via three steps: (1) We perform domain-specific Information Extraction to construct a knowledge graph (KG) from the target paper under review, a related work KG from the papers cited by the target paper, and a background KG from a large collection of previous papers in the domain. (2) By comparing these three KGs, we predict a review score and detailed structured knowledge as evidence for each review category. (3) We carefully select and generalize human review sentences into templates, and apply these templates to transform the review scores and evidence into natural language comments. Experimental results show that our review score predictor reaches 71.4%-100% accuracy. Human assessment by domain experts shows that 41.7%-70.5% of the comments generated by ReviewRobot are valid and constructive, and better than human-written ones for 20% of the time. Thus, ReviewRobot can serve as an assistant for paper reviewers, program chairs and authors.Comment: 14 pages. Accepted by The 14th International Conference on Natural Language Generation (INLG 2020) Code and resource is available at https://github.com/EagleW/ReviewRobo

Sixth SIGHAN Workshop on Chinese Language Processing Chinese Word Sense Disambiguation with PageRank and HowNet

Word sense disambiguation is a basic problem in natural language processing. This paper proposed an unsupervised word sense disambiguation method based PageRank and HowNet. In the method, a free text is firstly represented as a sememe graph with sememes as vertices and relatedness of sememes as weighted edges based on HowNet. Then UW-PageRank is applied on the sememe graph to score the importance of sememes. Score of each definition of one word can be computed from the score of sememes it contains. Finally, the highest scored definition is assigned to the word. This approach is tested on SENSEVAL-3 and the experimental results prove practical and effective.