TY - GEN
T1 - Sharing is Caring! Joint Multitask Learning Helps Aspect-Category Extraction and Sentiment Detection in Scientific Peer Reviews
AU - Kumar, Sandeep
AU - Ghosal, Tirthankar
AU - Bharti, Prabhat Kumar
AU - Ekbal, Asif
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The peer-review process is the benchmark of research validation. Peer-reviewed texts are the artifacts via which the editors/chairs decide the inclusion/exclusion of a paper in a journal or conference proceedings. Hence it is important for the editors/chairs to carefully analyze the peer-review text from various aspects of the paper (e.g., novelty, substance, soundness, etc.), identify the underlying sentiment of the reviewers, and thereby validate the informativeness of the reviews before making a decision. With the rise in research paper submissions, the current peer-review system is experiencing an unprecedented information overload. Sometimes it becomes stressful for the chairs/editors to make a reasonable decision within the stringent timelines. Here in this work, we attempt an interesting problem to automatically extract the aspect and sentiment from the peer-review texts. We design an end-to-end deep multitask learning model to perform aspect extraction and sentiment classification simultaneously. We show that both these tasks help each other in the predictions. We achieve encouraging performance on a recently released dataset of peer-review texts. We make our codes available for further research.
AB - The peer-review process is the benchmark of research validation. Peer-reviewed texts are the artifacts via which the editors/chairs decide the inclusion/exclusion of a paper in a journal or conference proceedings. Hence it is important for the editors/chairs to carefully analyze the peer-review text from various aspects of the paper (e.g., novelty, substance, soundness, etc.), identify the underlying sentiment of the reviewers, and thereby validate the informativeness of the reviews before making a decision. With the rise in research paper submissions, the current peer-review system is experiencing an unprecedented information overload. Sometimes it becomes stressful for the chairs/editors to make a reasonable decision within the stringent timelines. Here in this work, we attempt an interesting problem to automatically extract the aspect and sentiment from the peer-review texts. We design an end-to-end deep multitask learning model to perform aspect extraction and sentiment classification simultaneously. We show that both these tasks help each other in the predictions. We achieve encouraging performance on a recently released dataset of peer-review texts. We make our codes available for further research.
KW - aspect extraction
KW - deep learning
KW - peer review
KW - sentiment analysis
KW - text classification
UR - http://www.scopus.com/inward/record.url?scp=85124191933&partnerID=8YFLogxK
U2 - 10.1109/JCDL52503.2021.00081
DO - 10.1109/JCDL52503.2021.00081
M3 - Conference contribution
AN - SCOPUS:85124191933
T3 - Proceedings of the ACM/IEEE Joint Conference on Digital Libraries
SP - 270
EP - 273
BT - Proceedings - 2021 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2021
A2 - Downie, J. Stephen
A2 - McKay, Dana
A2 - Suleman, Hussein
A2 - Nichols, David M.
A2 - Poursardar, Faryaneh
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st ACM/IEEE Joint Conference on Digital Libraries, JCDL 2021
Y2 - 27 September 2021 through 30 September 2021
ER -