Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: KGQA task using T5-small with classic logical programming languages is presented.  The goal is to extract the representation of the question in the form of a Prolog query, which can then be used to answer the query programmatically. Using the MetaQA dataset, the presented method reaches 100% accuracy on the test dataset.
strengths_and_weaknesses: While the problem has been defined well, with some comparison to the literature, there are some very strong issues:
- 100% test accuracy is extremely unlikely for machine learning tasks unless the test data has been accidentally used to train the model. 
- The authors must provide strong arguments to accept 100% test accuracy, which is never heard of. 
- T5-small is used as the only large LM. Wonder how the other models will perform?
- The result table and Figure, which are required to follow the main text, are part of the body, NOT the appendix. As such, the page limit is not met. Also, Appendix C is the only referenced appendix. So how does the other relate? 
- Experimental setup and hyperparameters are not provided. Hence, reproducibility is hard. Especially the claimed 100% accuracy. 

suggested_changes: - The paper needs to be rewritten to fit the 2-page requirement while still using Appendix as additional material, NOT where the results are included. 
- Include publications published in the last two years as part of the literature review.
- Explain 100% accuracy with a clear experimental setup for reproducibility. 

This publication is a first step. But the authors need to clarify their method and results. Also, meet the basic requirements of a "Tiny" paper.
review_title: null

Decision: Invite to revise