Venue: ICLR 2023 TinyPapers

Reviews:

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 paper presents a framework for constructing a doocument-to-document network and demonstrate how various GNNs can be applied to this structure.
strengths_and_weaknesses: This is a nice contribution in the field of document representation based on graph modeling. The authors show that in scenarios where there are numerous classes and small number of samples, their approach works on par with current state of the art techniques. 
One scaling concern is that how practical is such an approach to large collections of documents.
suggested_changes: Since the there is a limit for this workshop (at most 2 pages of main text), I suggest making this work shorter and more concise. Moving some sections to an appendix would another option.

Typo: doocument -> document
review_title: null

Meta-review: metareview: The paper provides a novel framework for creating document representations from graphs of latent embeddings. It's an interesting approach to modeling document similarity that performs better in select circumstances, but is vastly outperformed by Transformer embedddings in most cases. 

As noted in the other review, the scaling concern in terms of large document sets is one that should be addressed, either in terms of actual time required or time complexity analysis.

Additionally, it might be valuable to create a comparison to a simpler model like a byte-pair embedding to see what improvements this system can create over that before going straight to Transformer style architectures.




confidence: 3: The area chair is somewhat confident
Decision: Invite to archive