Zero-Shot Event Detection via Identifying All Classes with Limited Prototypes
Authors: Zhe Kan, Li Pan, Zheng Wang


Reviewer paper summaries: The paper tackled the Event Detection task in the Zero-shot setting. The paper proposed a method called Minority-To-Majority. The paper claimed that the method doesn't require any additional annotation for the Zeroshot Learning. The method consists of 3 steps: Prototype Event Selection, Dataset Re-encoding, and Classification.
The paper shows experiments with improved performance to the baseline.
Summary of strengths: - The reported performance showed improvement on two datasets (ACE05 and FewEvent).
- The paper provided a theoretical analysis of the method.
- The paper included a comparison between clustering algorithms.
Summary of weaknesses: - From my understanding, the paper tried to reshuffle the dataset and reevaluate. (i.e., L179-181: "Finally, the prototypes selected from all clusters make up a subset that represents seen event types." The seen events are predefined unless all of these were done within the training set to simulate the zero-shot setting. This is a major flaw of the paper and the paper should not be published.
- The paper also resampled the evaluation data L460-468, hence making the reported performance incomparable with all previous work. The paper should report the performance in both settings for a fair comparison (i.e., report the performance on the old setting (maybe in the appendix) to show that the previous setting does not reveal the performance of unpopular classes)
- The paper is hard to follow. the definition of seen/unseen is not presented (e.g. Table 1) and is not predefined (e.g. 3.1).  (e.g., I am totally lost just reading the first sentence of Section 3.1. What are "all event prototypes"? Are they from the seen/unseen or from an unsupervised corpus?) The paper should be heavily changed to clarify the seen/unseen event types and their corresponding prototypes. The paper also should be rewritten to clarify how inference is done with the proposed method in the inference. (see https://aclanthology.org/P18-1201.pdf section 5.3)
- Many parts of the paper are excessively long and have so little novel information that I think they should be removed completely (e.g. L182-L204, L340-349). 
- L321 - L338: It is unclear to me why this is important. I feel like this part is for another paper, it has not been connected well with the rest of the paper.
Comments, suggestions and typos: - L65, L93: less -> fewer
- Table 1 would be helpful if it is positioned close to Algorithm 1.
- L298: "Upon" -> "upon"
- Citation: The use of relations between classes is similar to Few-shot Learning work (e.g., https://aclanthology.org/2021.emnlp-main.427.pdf models the relation between classes automatically without clustering)
Overall assessment scores: 1.5