Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: High Impact (HI): a submission which meets the reviewing criteria and is predicted to make an impact on the field
review_content: summary_of_contributions: This paper presents softEDA, a simple and novel method to augment text data and improve the performance of trained models. Advancing prior work, softEDA adds label smoothing to augmented samples. Experiment results highlight that softEDA significantly improves the model's performance comparing to existing augmentation methods.
strengths_and_weaknesses: ## Strengths and weaknesses

#### Clarity

1. Overall the paper is clear and well-written. I enjoy reading it!

#### Correctness

1. The claims and conclusions are justified by the findings.

#### Reproducibility

1. The paper includes findings from an empirical experiment. The code is open-sourced.

#### Follows basic requirements

1. This paper follows the basic formatting requirements.
suggested_changes: ## Suggested changes

1. In the experiments, it is unclear what traditional EDA methods are used before applying smooth labeling for softEDA. In other words, does softEDA use EDA or AEDA, or both?
2. SoftEDA is a simple method that can be generalized to a wide range of augmentation techniques. For future work, I would be very interested to see how it improves other types of augmentation methods (e.g., NL Augmenter[1]).

[1] Dhole, Kaustubh D., et al. "Nl-augmenter: A framework for task-sensitive natural language augmentation." arXiv preprint arXiv:2112.02721 (2021).
review_title: null

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: This work proposes SoftEDA, which follows the concept of easy data augmentation (EDA) to augment text data by perturbation then improves upon it to handle the uncertainty in the generated data augmentation. SoftEDA introduces uniform noise distribution across every class through label smoothing to provide the supervision in the form of soft labels instead of one-hot labels.
strengths_and_weaknesses: Strengths
- The paper is well-written and easy to read.
- The paper clearly presents a problem, explains the solution, and provides empirical results of the proposed solution on various datasets and model architectures.
- Experimental settings are described in detail. The code will also be made publicly available to encourage reproducibility.

Weaknesses
- Although the paper identifies the previous works’ (i.e., EDA and AEDA) inability to maintain sentence’s syntactic structure as a weakness, it is not clear how SoftEDA introduces any difference when it comes to this.
suggested_changes: - While it is understandable that Table 1 is a summarized overview of the results, Table 1 still should denote the 𝛼 used by SoftEDA for clarity.
- In the case of SoftEDA also being incapable of maintaining syntactic structure, it might be better to highlight SoftEDA’s advantages using another of the previous works’ existing gaps. In the case of SoftEDA being capable, it would be interesting to see further analysis that shows how the supervision of soft labels could be more beneficial compared to the hard labels for maintaining the syntactic structure.
review_title: null

Decision: Invite to present (notable)