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: The paper proposes an enhanced approach for transfer learning in extremely low-resource languages by using multiple source languages chosen in a data-driven manner and a sample selection strategy utilizing the languages in training through a multi-armed bandit algorithm.
strengths_and_weaknesses: Strengths.
- New methodology: The paper suggests a new technique for transfer learning in languages with a very low level of resources that makes use of numerous source languages and a multi-armed bandit algorithm for sample selection.
- State-of-the-art outcomes: The suggested method outperformed earlier approaches by achieving state-of-the-art outcomes on the named entity recognition test for low-resource languages, even with the same quantity of data.
- Data-driven approach: In low-resource environments, choosing source languages and samples based on data-driven criteria enables more effective use of scarce resources. 

Weaknesses.

- Lack of analysis: While presenting state-of-the-art results, the authors fail to offer a thorough justification for why their method is superior to earlier ones. 
- Reproducibility: The paper presents its findings in a  clear and correct manner. However, it might be hard for a reader to independently produce results

suggested_changes: 
- Adding additional analysis: Readers can gain a better understanding of how this new method operates and what makes it effective by reading a thorough examination of why their approach surpasses earlier approaches. 
- Increasing the repeatability of tests: To make it simple for others to replicate their work, authors might include detailed descriptions of the datasets utilized during research in addition to code samples.
review_title: null

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: Following the work of Xia et al., 2021 an enhanced approach is proposed which uses multiple source languages chosen in a data-driven manner. A sample selection strategy for utilizing the languages in training by using a multi armed bandit algorithm is also introduced. 
strengths_and_weaknesses: Very clearly written and presented paper. The findings are clear, and the basic requirements of the “Tiny” paper are also met. It’s a new perspective on existing work, and as such, the differences are clearly stated. 
suggested_changes: Only one main suggestion: Please read the instructions on Citations within the text.
review_title: null

Meta-review: metareview: An enhanced approach for transfer learning in extremely low-resource languages is presented. It uses multiple source languages chosen in a data-driven manner. A sample selection strategy for utilizing the languages in training using a multi-armed bandit algorithm is introduced. 

The proposed methodology, data-driven approach and competitive results in low-resource settings are collectively the strength of this paper.

Maybe a more clear explanation, in Appendix, for reproducibility will be nice to have. Fix the citations within the text according to the instructions for the “Tiny” paper.

confidence: 5: The area chair is absolutely certain
Decision: Invite to present (notable)