Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions: The paper presents a Sudanese-Arabic dialect model XLM-RoBERTa LM (Zol-RoBERTa). The model is trained on Sudanese-Arabic dialect.
strengths_and_weaknesses: Strengths

1. The model is trained on the Sudanese dialect spoken by 40 million people.

Weaknesses

1. Novelty is weak. Previous work (SudaBERT) exists on this dialect. The authors pretrained with MLM and fine-tuned on the sentiment analysis task using standard method proposed on BERT paper, with no new methods. 
2. Methodology is weak. The model is trained only for 1 setting from pre-existing "xlm-roberta-base" checkpoint. There are no other ablations experimented with that would suggest specific properties of the model. 
3. Evaluation is weak. The model is only evaluated on one sentiment classification task and reports Accuracy. 
4. Punctuations are sometimes missing, regarding quotation marks or end of sentences. 
5. There are no citations for other previous multi-lingual fine-tuning methods which the paper is inspired from.
suggested_changes: 1. The comparison between SudaBERT should be done in this paper, not left as future work.
2. Please evaluate the model on at least 2 ablations.
3. Please evaluate the model on at least 2 tasks.
4. Please revise the paper with grammatical tools.
5. Please add citations for other previous multi-lingual fine-tuning methods.
6. Please clarify the choice of the XLM-Roberta model, in terms of tokenization and capabilities for the Sudanese dialect.
7. No code or data release plans are mentioned in the document. If possible, I suggest authors release their contribution.
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The paper presents a solution of Sudanese dialect language analysis through encoding of the language where they have used 6 million collected sentences.
strengths_and_weaknesses: **Strength**\
the paper provide solution to Arabic language understanding through the use of transformers especially encoding through using large collected data.\
**Weakness**
*  I wonder upon the use XLM-RoBERTa doesn't the paper use the pretrained model encoders where I assume the study performed transfer learning instead of encoding. 
* In order to use XLM-RoBERTa, tokenizers must be used, so I wonder if most of the tokens weren't transformed into [unk] tokens? as happens to most of pretrained models for a NLP task.
suggested_changes: Convincing explanation of difference between transfer learning and encoding where I believe the study performed transfer learning using XLM-RoBERTa
review_title: null

Meta-review: metareview: **Summary**

* The paper presents a trained XLM-RoBERTa model on a Sudanese-Arabic dialect. They used 6 million sentences to train the model. 

**Strength**
* The work is done in a low-resourced dialect which is Sudanese.
 
**Weakness**
* Previous works on the Sudanese dialect exist (i.e. SudaBERT). And the comparison to that should be included in this paper instead of future work.
* The training and evaluation settings are weak. It would be good to evaluate the method on multiple tasks.
* Citations of other multi-lingual methods are missing.
* The codes / or data used are not released or no plans are mentioned in doing that.


confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to revise