Multi-Stage Prompt Tuning for Political Perspective Detection in Low-Resource Settings

Kang Min Kim, Mingyu Lee, Hyun Sik Won, Min Ji Kim, Yeachan Kim, Sang Keun Lee

Research output: Contribution to journalArticlepeer-review

Abstract

Political perspective detection in news media—identifying political bias in news articles—is an essential but challenging low-resource task. Prompt-based learning (i.e., discrete prompting and prompt tuning) achieves promising results in low-resource scenarios by adapting a pre-trained model to handle new tasks. However, these approaches suffer performance degradation when the target task involves a textual domain (e.g., a political domain) different from the pre-training task (e.g., masked language modeling on a general corpus). In this paper, we develop a novel multi-stage prompt tuning framework for political perspective detection. Our method involves two sequential stages: a domain- and task-specific prompt tuning stage. In the first stage, we tune the domain-specific prompts based on a masked political phrase prediction (MP3) task to adjust the language model to the political domain. In the second task-specific prompt tuning stage, we only tune task-specific prompts with a frozen language model and domain-specific prompts for downstream tasks. The experimental results demonstrate that our method significantly outperforms fine-tuning (i.e., model tuning) methods and state-of-the-art prompt tuning methods on the SemEval-2019 Task 4: Hyperpartisan News Detection and AllSides datasets.

Original languageEnglish
Article number6252
JournalApplied Sciences (Switzerland)
Volume13
Issue number10
DOIs
Publication statusPublished - 2023 May

Bibliographical note

Funding Information:
This work was supported by the Basic Research Program through the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C3010430), the NRF grant funded by the Korea government (MSIT) (No. 2022R1C1C1010317), the Catholic University of Korea (Research Fund, 2021), and Institute of Information communications Technology Planning Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Program (Korea University)).

Publisher Copyright:
© 2023 by the authors.

Keywords

  • political bias detection
  • pre-trained language model
  • prompt tuning
  • prompt-based learning
  • self-supervised learning

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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