Rationale-Guided Text Classification System

A Rationale-Guided Text Classification System is a text classification system that is an interpretable NLP system implementing select-then-classify architectures to solve rationale-guided text classification tasks.

• AKA: Select-Then-Classify System, Rationale-Based Classifier.
• Context:
  • It can typically implement Selector Modules for rationale extraction.
  • It can typically implement Classifier Modules operating on selected rationales.
  • It can typically enforce Information Bottlenecks between selector and classifier.
  • It can typically maintain Rationale Discreteness for human interpretability.
  • It can typically provide Rationale Visualizations in user interfaces.
  • ...
  • It can often use Reinforcement Learning for discrete selection training.
  • It can often employ Gumbel-Softmax for differentiable selection.
  • It can often incorporate Length Penaltys for concise rationales.
  • It can often implement Multi-Head Selection for diverse rationales.
  • ...
  • It can range from being a Pipeline Rationale System to being an Joint Rationale System, depending on its training approach.
  • It can range from being a Hard Selection System to being a Soft Attention System, depending on its selection mechanism.
  • ...
  • It can process Text Documents requiring interpretable classification.
  • It can output Classification Labels with highlighted rationales.
  • It can be evaluated by Rationale-Based Classification Measures.
  • It can integrate with Explainable AI Platforms.
  • ...
• Example(s):
  • FRESH System using frozen selectors with importance scores.
  • Lei et al. Rationale System with reinforcement learning selector.
  • LIME-Based Classifiers generating post-hoc rationales.
  • Attention-Based Rationale Systems with discrete attention.
  • Human-in-the-Loop Rationale Systems with interactive selection.
  • ...
• Counter-Example(s):
  • Black-Box Neural Classifiers, which lack rationale generation.
  • Full-Text Classifiers, which process entire documents without selection.
  • Rule-Based Classifiers, which use predefined patterns not learned rationales.
• See: Interpretable Classification System, Explainable NLP System, Evidence-Based Classifier, Select-Then-Predict System.

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