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Human-annotated rationales and explainable text classification: a survey.

Elize Herrewijnen1,2, Dong Nguyen1, Floris Bex1,3

  • 1Department of Information & Computing Sciences, Utrecht University, Utrecht, Netherlands.

Frontiers in Artificial Intelligence
|June 10, 2024
PubMed
Summary
This summary is machine-generated.

Annotator rationales, or explanations for data labels, enhance machine learning model quality. These human-generated insights also aid in developing artificial intelligence explanations.

Keywords:
annotator rationalesdata collectionexplainable artificial intelligencehuman-annotated rationalesmachine learningnatural language explanationsrationale agreementtext classification

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Natural Language Processing

Background:

  • Data annotation is crucial for training machine learning models.
  • Understanding the reasoning behind annotations can improve model performance and interpretability.
  • Current methods often lack detailed explanations for classification decisions.

Purpose of the Study:

  • To survey the methods for collecting and utilizing annotator rationales.
  • To highlight the benefits of human-annotated rationales in machine learning.
  • To explore the role of rationales in advancing explainable artificial intelligence.

Main Methods:

  • Literature review of studies involving annotator rationales.
  • Analysis of the impact of rationales on data quality.
  • Examination of the use of rationales in model development and evaluation.

Main Results:

  • Human-annotated rationales significantly improve data quality.
  • Rationales serve as a valuable resource for enhancing machine learning models.
  • Annotator rationales inspire the creation and assessment of model-generated rationales.

Conclusions:

  • Annotator rationales are essential for high-quality data and improved AI models.
  • The study of rationales is key to developing more explainable artificial intelligence.
  • Future work should focus on leveraging rationales for both human and machine learning.