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: The paper proposes to use pre-trained models to predict text complexity in Russian and studies whether the length of the segment used during training/inference impacts complexity prediction.
strengths_and_weaknesses: Strengths: Text complexity prediction is an important task in NLP and is a preliminary step in identifying when and how a text should be simplified. This can help make texts more accessible to people with various reading or cognitive disabilities. The paper proposes to address this task in Russian.

Weaknesses: The paper lacks a clear description of the experimental setup: a) how the fragment length is computed (bpe-level/word-level) and b) how the pre-trained models (ex: xlm-roberta-large-en-ru)  were trained and c) how the representation is used to train the regressor as there are many different ways and that could also impact the results (for ex: CLS embeddings vs Mean-pooling for bert based models). 
suggested_changes: Adding a clear motivation on the experimental design choices and the choice of hypothesis will help better understand and explain the results.
review_title: null

Meta-review: metareview: The researchers fine-tuned three pre-trained BERT models (from Hugging face) on russian text, using Mean Absolute Error (MAE) as the primary evaluation metric. Their findings revealed that there was no significant correlation between text fragment size and the models performance.




confidence: 5: The area chair is absolutely certain
Decision: Invite to revise