Abstract：Sharing is CaringJoint Multitask Learning Helps Aspect-Category Extraction and Sentiment Detection in Scientific Peer Reviews The peer-review process is the benchmark of research validationPeer-reviewed texts are the artifacts via which the editorschairs decide the inclusionexclusion of a paper in a journal or conference proceedingsHence it is important for the editorscha Retrieval and IndexingBecause the technology can automatically extract important aspects and sentiments from the literatureit enhances the retrieval systemThis study mainly focuses on using deep learning and machine learning techniques for natural language processingNLPtasksSpecificallyit attempts to automatically extract various aspects of papersegnoveltysignificancesoundnessetcand the sentiments of the reviews from peer review textsTo this endthey designed an end-to-end deep multi-task learning model that performs aspect extraction and sentiment classification simultaneously