TY - GEN
T1 - DeepSentipeer
T2 - 57th Annual Meeting of the Association for Computational Linguistics, ACL 2019
AU - Ghosal, Tirthankar
AU - Verma, Rajeev
AU - Ekbal, Asif
AU - Bhattacharyya, Pushpak
N1 - Publisher Copyright:
© 2019 Association for Computational Linguistics
PY - 2020
Y1 - 2020
N2 - Automatically validating a research artefact is one of the frontiers in Artificial Intelligence (AI) that directly brings it close to competing with human intellect and intuition. Although criticized sometimes, the existing peer review system still stands as the benchmark of research validation. The present-day peer review process is not straightforward and demands profound domain knowledge, expertise, and intelligence of human reviewer(s), which is somewhat elusive with the current state of AI. However, the peer review texts, which contains rich sentiment information of the reviewer, reflecting his/her overall attitude towards the research in the paper, could be a valuable entity to predict the acceptance or rejection of the manuscript under consideration. Here in this work, we investigate the role of reviewers sentiments embedded within peer review texts to predict the peer review outcome. Our proposed deep neural architecture takes into account three channels of information: the paper, the corresponding reviews, and the review polarity to predict the overall recommendation score as well as the final decision. We achieve significant performance improvement over the baselines (~ 29% error reduction) proposed in a recently released dataset of peer reviews. An AI of this kind could assist the editors/program chairs as an additional layer of confidence in the final decision making, especially when non-responding/missing reviewers are frequent in present day peer review.
AB - Automatically validating a research artefact is one of the frontiers in Artificial Intelligence (AI) that directly brings it close to competing with human intellect and intuition. Although criticized sometimes, the existing peer review system still stands as the benchmark of research validation. The present-day peer review process is not straightforward and demands profound domain knowledge, expertise, and intelligence of human reviewer(s), which is somewhat elusive with the current state of AI. However, the peer review texts, which contains rich sentiment information of the reviewer, reflecting his/her overall attitude towards the research in the paper, could be a valuable entity to predict the acceptance or rejection of the manuscript under consideration. Here in this work, we investigate the role of reviewers sentiments embedded within peer review texts to predict the peer review outcome. Our proposed deep neural architecture takes into account three channels of information: the paper, the corresponding reviews, and the review polarity to predict the overall recommendation score as well as the final decision. We achieve significant performance improvement over the baselines (~ 29% error reduction) proposed in a recently released dataset of peer reviews. An AI of this kind could assist the editors/program chairs as an additional layer of confidence in the final decision making, especially when non-responding/missing reviewers are frequent in present day peer review.
UR - http://www.scopus.com/inward/record.url?scp=85084073691&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85084073691
T3 - ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
SP - 1120
EP - 1130
BT - ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PB - Association for Computational Linguistics (ACL)
Y2 - 28 July 2019 through 2 August 2019
ER -