Sharing is Caring! Joint Multitask Learning Helps Aspect-Category Extraction and Sentiment Detection in Scientific Peer Reviews

Sandeep Kumar, Tirthankar Ghosal, Prabhat Kumar Bharti, Asif Ekbal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2021 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2021
EditorsJ. Stephen Downie, Dana McKay, Hussein Suleman, David M. Nichols, Faryaneh Poursardar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages270-273
Number of pages4
ISBN (Electronic)9781665417709
DOIs
StatePublished - 2021
Externally publishedYes
Event21st ACM/IEEE Joint Conference on Digital Libraries, JCDL 2021 - Virtual, Online, United States
Duration: Sep 27 2021Sep 30 2021

Publication series

NameProceedings of the ACM/IEEE Joint Conference on Digital Libraries
Volume2021-September
ISSN (Print)1552-5996

Conference

Conference21st ACM/IEEE Joint Conference on Digital Libraries, JCDL 2021
Country/TerritoryUnited States
CityVirtual, Online
Period09/27/2109/30/21

Keywords

  • aspect extraction
  • deep learning
  • peer review
  • sentiment analysis
  • text classification

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