-	Very difficult to follow the motivation of this paper. And it looks like an incremental engineering paper.
-	The abstract looks a little vague. For example, “However, it is difficult to fully model interaction between utterances …” What is 'interaction between utterances' and why is it difficult to model? This information is not evident from the previous context. Additionally, the misalignment between the two views might seem obvious since most ERC models aggregate information using methods like residual connections. So, why the need for alignment? Isn't the goal to leverage the advantages of both features? Or does alignment help in achieving a balance between both features?
-	The author used various techniques to enhance performance, including contrastive learning, external knowledge, and graph networks. However, these methods seem contradictory to the limited experiments conducted. For example, the author proposes a new semi-parametric inferencing paradigm involving memorization to address the recognition problem of tail class samples. However, the term "semi-parametric" is not clearly defined, and there is a lack of experimental evidence to support the effectiveness of the proposed method in tackling the tail class samples problem.
-	The Related Work section lacks a review of self-supervised contrastive learning in ERC.
-	The most recent comparative method is still the preprint version available on ArXiv, which lacks convincing evidence.
-	Table 3 needs some significance tests to further verify the assumptions put forward in the paper.
-	In Chapter 5.3, the significant impact of subtle hyperparameter fluctuations on performance raises concerns about the method's robustness. The authors could consider designing an automated hyperparameter search mechanism or decoupling dependencies on these hyperparameters to address this.
-	There is a lack of error analysis.
-	The formatting of the reference list is disorganized and needs to be adjusted.
-	Writing errors are common across the overall paper.
- Very difficult to follow the motivation of this paper. And it looks like an incremental engineering paper.