1. The write-up has many typos and some formulas/explanations are confusing.
2. The technical innovation of the proposed method is limited. The proposed objective function is basically a combination of two related works with tiny changes.
3. Reproductivity is not ideal, as some essential parts are not addressed in the paper, such as training data.
4. More strong baselines should be included/discussed in the experiments.
Comments 1. Line 18: it’s better to specify the exact tasks rather than stating several tasks in the abstract 2. Line 69: “current” -> “currently” 3. Line 112: margin-based loss can use one positive with MULTIPLE negatives.
4. Figure 1: the relation types are not explicitly modeled in this work so the figure is kind of confusing.
5. Eq. 1: what is z_p 6. Eq. 2: how do you convert BERT token embeddings into event embeddings? Concatenation? Any pooling?
7. Line 236-237: “conciser” -> “consider” 8. Line 209-211: I can understand what you mean but this part should be re-written. For example, “given an anchor event, we generate 3 positive samples with different dropout masks.”
9. Table 1: needs to include a random baseline to show the task difficulty 10. Experiments: what training data you use for pre-training?
11. Table 1: do the baselines and the proposed method use the same training data for pre-training? How did you get the results for the baselines? Did you have your own implementation, or directly use their released embeddings/code?
12. Line 450: L_{pc} or L_{cp}| in Eq. 7?
13. Table 2: what’s the difference between “SWCC w/o Prototype-based Clustering” and “BERT(InfoNCE)”?
14. Table 3: MCNC should have many strong baselines that are not compared here, such as the baselines in [1]. Can you justify the reason?
15. Can you provide an analysis on the impact of the number of augmented samples (e.g., z_{a1}, z_{a2}) here?
14. Table 3: MCNC should have many strong baselines that are not compared here, such as the baselines in [1]. Can you justify the reason?