Comparative Study of Single-task and Multi-task Learning on Research Protocol Document Classification

Abid Famasya Abdillah, Mohammad Zaenuddin Hamidi, Ratih Nur Esti Anggraeni, Riyanarto Sarno

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

1 Citation (Scopus)

Abstract

Research protocol is an important document to be scrutinized by the ethical committee. As the research proposal is growing, the necessity for quick and concise protocol review is rising. This study undergoes a comparative study of multi-task learning (MTL) and single-task learning (STL) to classify research protocol documents. We try to carry out the classification process from the summary of health research. We represent research documents as multi-label classification problems and develop a deep learning model based on MTL and STL strategies. In our evaluation, multi-task learning achieved a better result with 0.125 loss and 0.785 Jaccard score than 0.182 and 0.720 in single-task learning. In consequence, MTL has a 27% slower computation time than STL.

Original languageEnglish
Title of host publicationProceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages213-217
Number of pages5
ISBN (Electronic)9781665440592
DOIs
Publication statusPublished - 2021
Event13th International Conference on Information and Communication Technology and System, ICTS 2021 - Virtual, Online, Indonesia
Duration: 20 Oct 202121 Oct 2021

Publication series

NameProceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021

Conference

Conference13th International Conference on Information and Communication Technology and System, ICTS 2021
Country/TerritoryIndonesia
CityVirtual, Online
Period20/10/2121/10/21

Keywords

  • Multi-task learning
  • Multilabel document classification
  • Research protocol classification
  • Single-task learning

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