Overview and Insights from Scope Detection of the Peer Review Articles Shared Tasks 2021

Reddy, Saichethan Miriyala; Saini, Naveen

doi:10.1007/978-3-030-75015-2_7

Cited by 6 publications

(2 citation statements)

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“…The dataset [13] for the shared task of The First Workshop and Shared Task on Scope Detection of the Peer Review Articles (SPDRA 2021) [14] consists of 16,800 training instances, 11,200 validation instances and 7,000 test instances. They belong to seven classes as described in Table 1.…”

Section: Data Descriptionmentioning

confidence: 99%

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Using Transformer based Ensemble Learning to classify Scientific Articles

Ghosh,

Chopra

2021

Preprint

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Many time reviewers fail to appreciate novel ideas of a researcher and provide generic feedback. Thus, proper assignment of reviewers based on their area of expertise is necessary. Moreover, reading each and every paper from end-to-end for assigning it to a reviewer is a tedious task. In this paper, we describe a system which our team FideLIPI submitted in the shared task of SDPRA-2021 1 [14]. It comprises four independent sub-systems capable of classifying abstracts of scientific literature to one of the given seven classes. The first one is a RoBERTa [10] based model built over these abstracts. Adding topic models / Latent dirichlet allocation (LDA) [2] based features to the first model results in the second sub-system. The third one is a sentence level RoBERTa [10] model. The fourth one is a Logistic Regression model built using Term Frequency Inverse Document Frequency (TF-IDF) features. We ensemble predictions of these four sub-systems using majority voting to develop the final system which gives a F1 score of 0.93 on the test and validation set. This outperforms the existing State Of The Art (SOTA) model SciBERT's [1] in terms of F1 score on the validation set.

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Section: Data Descriptionmentioning

confidence: 99%

“…Equal Contribution Equal Contribution 1 https://sdpra-2021.github.io/website/ (accessed January 25, 2021) 2 https://www.pakdd2021.org/ (accessed January 25, 2021) [14]. This paper narrates the approach our team FideLIPI followed while participating in this challenge.…”

Section: Introductionmentioning

confidence: 99%