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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| Veröffentlicht in: | 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) S. 270 - 273 |
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| Sprache: | Englisch |
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01.09.2021
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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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Sandeep surname: Kumar fullname: Kumar, Sandeep email: sandeep_2121cs29@iitp.ac.in organization: IIT Patna,CSE Department,India – sequence: 2 givenname: Tirthankar surname: Ghosal fullname: Ghosal, Tirthankar email: ghosal@ufal.mff.cuni.cz organization: UFAL, MFF Charles University,CZ – sequence: 3 givenname: Prabhat Kumar surname: Bharti fullname: Bharti, Prabhat Kumar email: prabhat_1921cs32@iitp.ac.in organization: IIT Patna,CSE Department,India – sequence: 4 givenname: Asif surname: Ekbal 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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