1. The writing needs to be improved. Structurally, there should be a "Related Work" section which would inform the reader that this is where prior research has been done, as well as what differentiates the current work with earlier work. A clear separation between the "Introduction" and "Related Work" sections would certainly improve the readability of the paper.
2. The paper does not compare the results with some of the earlier research work from 2020. While the authors have explained their reasons for not doing so in the author response along the lines of "Those systems are not state-of-the-art", they have compared the results to a number of earlier systems with worse performances (Eg. Taghipour and Ng (2016)).
Comments: 1. Please keep a separate "Related Work" section. Currently "Introduction" section of the paper reads as 2-3 paragraphs of introduction, followed by 3 bullet points of related work and again a lot of introduction. I would suggest that you shift those 3 bullet points ("Traditional AES", "Deep Neural AES" and "Pre-training AES") to the Related work section.
2. Would the use of feature engineering help in improving the performance? Uto et al. (2020)'s system reaches a QWK of 0.801 by using a set of hand-crafted features. Perhaps using Uto et al. (2020)'s same feature set could also improve the results of this work.
3. While the out of domain experiment is pre-trained on other prompts, it is still fine-tuned during training on the target prompt essays. Typos: 1. In Table #2, Row 10, the reference for R2BERT is Yang et al. (2020), not Yang et al. (2019).
Missing References: 1. Panitan Muangkammuen and Fumiyo Fukumoto. " Multi-task Learning for Automated Essay Scoring with Sentiment Analysis". 2020. In Proceedings of the AACL-IJCNLP 2020 Student Research Workshop.
2. Sandeep Mathias, Rudra Murthy, Diptesh Kanojia, Abhijit Mishra, Pushpak Bhattacharyya. 2020. Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour. In Proceedings of the 2020 AACL-IJCNLP Main Conference.
2. Would the use of feature engineering help in improving the performance? Uto et al. (2020)'s system reaches a QWK of 0.801 by using a set of hand-crafted features. Perhaps using Uto et al. (2020)'s same feature set could also improve the results of this work.