Abstract
One key frontier of artificial intelligence (AI) is the ability to comprehend research articles and validate their findings, posing a magnanimous problem for AI systems to compete with human intelligence and intuition. As a benchmark of research validation, the existing peer-review system still stands strong despite being criticized at times by many. However, the paper vetting system has been severely strained due to an influx of research paper submissions and increased conferences/journals. As a result, problems, including having insufficient reviewers, finding the right experts, and maintaining review quality, are steadily and strongly surfacing. To ease the workload of the stakeholders associated with the peer-review process, we probed into what an AI-powered review system would look like. In this work, we leverage the interaction between the paper’s full text and the corresponding peer-review text to predict the overall recommendation score and final decision. We do not envisage AI reviewing papers in the near future. Still, we intend to explore the possibility of a human–AI collaboration in the decision-making process to make the current system FAIR. The idea is to have an assistive decision-making tool for the chairs/editors to help them with an additional layer of confidence, especially with borderline and contrastive reviews. We use a deep attention network between the review text and paper to learn the interactions and predict the overall recommendation score and final decision. We also use sentiment information encoded within peer-review texts to guide the outcome further. Our proposed model outperforms the recent state-of-the-art competitive baselines. We release the code of our implementation here: https://github.com/PrabhatkrBharti/PEERRec.git.
Original language | English |
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Pages (from-to) | 55-72 |
Number of pages | 18 |
Journal | International Journal on Digital Libraries |
Volume | 25 |
Issue number | 1 |
DOIs | |
State | Published - Mar 2024 |
Externally published | Yes |
Funding
The first author, Prabhat Kumar Bharti, acknowledges Quality Improvement Programme, an initiative of All India Council for Technical Education (AICTE), Government of India, and Asif Ekbal, the fourth author, has been awarded the Visvesvaraya Young Faculty Award. The authors wish to acknowledge and thank Digital India Corporation and the Ministry of Electronics and Information Technology, Government of India, for their support. Tirthankar Ghosal acknowledges Cactus Communications, India.
Funders | Funder number |
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Asif Ekbal | |
Digital India Corporation | |
All India Council for Technical Education | |
Ministry of Electronics and Information technology |
Keywords
- Attention mechanism
- Decision prediction
- Deep neural network
- Peer reviews
- Recommendation score prediction