Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 1: strong reject
review_content: summary_of_the_paper: The paper provides a comparison of data distillation and active learning for text classification. The models used in the paper are the most common types of distillation and active learning models. Experiments are performed on the ST dataset. Discussions are offered on the results.
strength_and_weaknesses: Strengths:
1. Good references to past papers that propose the ideas.
2. Clear introduction on the technical details.

Weaknesses:
1. No novel ideas are proposed in the paper.
2. Only one small dataset is used for experiments.
3. Discussions lack depth.
clarity,_quality,_novelty_and_reproducibility: The paper contains a large number of English grammar errors, making it sometimes hard to read.

There is no novelty in the paper. Experiments are limited to one datasets.

I have no reason to doubt the reproducibility of the experimental results.
summary_of_the_review: No novelty. Strightforward comparison between distillation and active learning. Limited results and discussions.
review_title: null

final_score: rating: 1: strong reject
review_content: summary_of_the_paper: This paper introduces and compares discrepancies of Data Distillation and Active Learning and evaluate them on NLP classification dataset: Stanford Sentiment Treebank.
strength_and_weaknesses: Strength:
1. This is a good introductory article to compare Data Distillation and Active Learning.

Weakness:
1. This paper did not propose any new methodology or dataset.
2. There is only one evaluation dataset and two sets of experiments.
clarity,_quality,_novelty_and_reproducibility: Data distillation and active learning are explained clearly.
summary_of_the_review: This is a good introductory article to compare Data Distillation and Active Learning. But it did not propose any new methodology or dataset. And there are no new dataset proposed nor enough experiments to serve as benchmark.
review_title: null

final_score: rating: 1: strong reject
review_content: summary_of_the_paper: This paper compares dataset distillation and active learning in text classification. Authors introduce basic methods and conduct experiments on the SST dataset. 
strength_and_weaknesses: The research objective of this paper is neither clear nor reasonable. Experimental results of active learning are not reliable. The conclusion is not reasonable based on the simple experiments. 
clarity,_quality,_novelty_and_reproducibility: This paper is of low quality. 
summary_of_the_review: This paper is below the standard of ICLR.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: This paper introduces a comparison of dataset distillation and active learning in text classification. Reviewers had numerous concerns about the paper but the authors did not respond to them. Therefore, I would like to reject the paper.
Decision: Reject