TY - GEN
T1 - Attend to Your Review
T2 - 28th International Conference on Neural Information Processing, ICONIP 2021
AU - Verma, Rajeev
AU - Shinde, Kartik
AU - Arora, Hardik
AU - Ghosal, Tirthankar
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Peer-review process is fraught with issues like bias, inconsistencies, arbitrariness, non-committal weak rejects, etc. However, it is anticipated that the peer reviews provide constructive feedback to the authors against some aspects of the paper such as Motivation/Impact, Soundness/Correctness, Novelty, Substance, etc. A good review is expected to evaluate a paper under the lens of these aspects. An automated system to extract these implicit aspects from the reviews would help determine the quality/goodness of the peer review. In this work, we propose a deep neural architecture to extract the aspects of the paper on which the reviewer commented in their review. Our automatic aspect-extraction model based on BERT and neural attention mechanism achieves superior performance over the standard baselines. We make our codes, analyses and other matrials available at https://github.com/cruxieu17/aspect-extraction-peer-reviews.
AB - Peer-review process is fraught with issues like bias, inconsistencies, arbitrariness, non-committal weak rejects, etc. However, it is anticipated that the peer reviews provide constructive feedback to the authors against some aspects of the paper such as Motivation/Impact, Soundness/Correctness, Novelty, Substance, etc. A good review is expected to evaluate a paper under the lens of these aspects. An automated system to extract these implicit aspects from the reviews would help determine the quality/goodness of the peer review. In this work, we propose a deep neural architecture to extract the aspects of the paper on which the reviewer commented in their review. Our automatic aspect-extraction model based on BERT and neural attention mechanism achieves superior performance over the standard baselines. We make our codes, analyses and other matrials available at https://github.com/cruxieu17/aspect-extraction-peer-reviews.
KW - Aspect extraction
KW - Deep neural networks
KW - Peer reviews
UR - https://www.scopus.com/pages/publications/85121917270
U2 - 10.1007/978-3-030-92310-5_88
DO - 10.1007/978-3-030-92310-5_88
M3 - Conference contribution
AN - SCOPUS:85121917270
SN - 9783030923099
T3 - Communications in Computer and Information Science
SP - 761
EP - 768
BT - Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings
A2 - Mantoro, Teddy
A2 - Lee, Minho
A2 - Ayu, Media Anugerah
A2 - Wong, Kok Wai
A2 - Hidayanto, Achmad Nizar
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 8 December 2021 through 12 December 2021
ER -