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

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...

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Vydáno v:2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) s. 270 - 273
Hlavní autoři: Kumar, Sandeep, Ghosal, Tirthankar, Bharti, Prabhat Kumar, Ekbal, Asif
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.09.2021
Témata:
aspect extraction
Benchmark testing
Codes
deep learning
Libraries
peer review
sentiment analysis
Task analysis
text classification
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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 1 1 https://www.iitp.ac.in/~ai-nlp-ml/resources.html#aspect-category-sentiment.
AbstractList 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 1 1 https://www.iitp.ac.in/~ai-nlp-ml/resources.html#aspect-category-sentiment.
Author Kumar, Sandeep
Ekbal, Asif
Bharti, Prabhat Kumar
Ghosal, Tirthankar
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  fullname: Ekbal, Asif
  email: asif@iitp.ac.in
  organization: IIT Patna,CSE Department,India
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SubjectTerms aspect extraction
Benchmark testing
Codes
deep learning
Libraries
peer review
sentiment analysis
Task analysis
text classification
Title Sharing is Caring! Joint Multitask Learning Helps Aspect-Category Extraction and Sentiment Detection in Scientific Peer Reviews
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