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: LSTM-ATT performance with different feature combinations
strengths_and_weaknesses: The paper focuses on performance of only LSTM-ATT with additional features(latest job & skills)
Considering one of the authors meets URM criteria, this is a good start.
Scope for improvement would be focus on optimization of LSTM-ATT and for future compare the performance of LSTM-ATT & Hybrid CNN used in mentioned reference paper Wan& Ye,2022 

suggested_changes: Request both authors to discuss and improve their paper with more research
1. Authors can also focus on optimization of the LSTM-ATT with hyperparameter tuning and compare performance.
2. For future papers, Ideal comparison would be performance two or more model techniques rather than just features selected.
review_title: null

Meta-review: metareview: 1. LSTM-ATT performance based on feature selection is clear and crisp
2. The paper provides architecture, and results of their research
3. The paper though well explained fails to provide code or data to make the findings reproducible
4. The submission follows basic requirements

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