Natural Language Generation (NLG) Performance Measure

A Natural Language Generation (NLG) Performance Measure is a linguistic processing performance measure that evaluates NLG system effectiveness for natural language generation (NLG) tasks.

• AKA: NLG Evaluation Metric, NLG Quality Measure, Language Generation Performance Metric, Text Generation Evaluation Measure.
• Context:
  • It can typically assess NLG Text Fluency through NLG grammaticality metrics.
  • It can typically evaluate NLG Text Coherence through NLG discourse structure analysis.
  • It can typically measure NLG Factual Accuracy through NLG fact verification metrics.
  • It can typically determine NLG Text Relevance through NLG content alignment scoring.
  • It can typically quantify NLG Text Diversity through NLG lexical variety metrics.
  • It can often support NLG System Improvement through NLG diagnostic feedback.
  • It can often enable NLG Model Comparison through NLG standardized benchmarks.
  • It can often facilitate NLG Quality Control through NLG acceptance thresholds.
  • It can often measure NLG Semantic Adequacy through NLG meaning preservation metrics.
  • It can often assess NLG Style Consistency through NLG stylistic analysis.
  • It can range from being an Intrinsic NLG Performance Measure to being an Extrinsic NLG Performance Measure, depending on its NLG evaluation context.
  • It can range from being a Reference-Based NLG Performance Measure to being a Reference-Free NLG Performance Measure, depending on its NLG comparison approach.
  • It can range from being a Manual NLG Performance Measure to being an Automated NLG Performance Measure, depending on its NLG evaluation methodology.
  • It can range from being an Objective NLG Performance Measure to being a Subjective NLG Performance Measure, depending on its NLG measurement approach.
  • It can range from being a Single-Aspect NLG Performance Measure to being a Multi-Aspect NLG Performance Measure, depending on its NLG evaluation scope.
  • It can integrate with NLG Benchmark Datasets for NLG standardized testing.
  • It can support NLG Research Tasks through NLG experimental validation.
  • ...
• Examples:
  • Core NLG Performance Measures, such as:
    • NLG Fluency Measures for NLG grammatical correctness.
    • NLG Coherence Measures for NLG logical flow.
    • NLG Adequacy Measures for NLG content completeness.
    • NLG Diversity Measures for NLG vocabulary richness.
  • Automatic NLG Performance Measures, such as:
    • BLEU Score for NLG n-gram precision.
    • ROUGE Score for NLG recall-oriented evaluation.
    • METEOR Score for NLG flexible matching.
    • CIDEr Score for NLG consensus evaluation.
    • BERTScore for NLG contextual embedding similarity.
    • BLEURT for NLG learned metric evaluation.
    • MoverScore for NLG semantic distance.
    • Perplexity for NLG language model quality.
  • Task-Specific NLG Performance Measures, such as:
    • Machine Translation Performance Measures, such as:
      • chrF Score for NLG character-level evaluation.
      • TER (Translation Edit Rate) for NLG edit distance.
      • COMET Score for NLG neural translation evaluation.
    • Text Summarization Performance Measures, such as:
      • ROUGE-L for NLG summary coverage.
      • Pyramid Score for NLG content unit evaluation.
      • SummaQA for NLG summary consistency.
    • Dialogue Generation Performance Measures, such as:
      • ADEM Score for NLG dialogue appropriateness.
      • USR Score for NLG dialogue understanding.
      • FED Score for NLG dialogue quality.
    • Story Generation Performance Measures, such as:
      • Story Coherence Score for NLG narrative flow.
      • Character Consistency Metric for NLG story logic.
  • Human-Based NLG Performance Measures, such as:
    • NLG Human Evaluation Scores for NLG subjective quality.
    • NLG Likert Scale Ratings for NLG preference assessment.
    • NLG Ranking Evaluations for NLG comparative analysis.
    • NLG Error Annotations for NLG fine-grained feedback.
  • Factuality NLG Performance Measures, such as:
    • FactCC for NLG factual consistency.
    • PARENT Score for NLG table-to-text accuracy.
    • DAE Score for NLG entailment verification.
  • ...
• Counter-Examples:
  • NLU Performance Measure, which evaluates language understanding rather than NLG text production.
  • Information Retrieval Performance Measure, which assesses document retrieval rather than NLG text generation.
  • Speech Recognition Performance Measure, which measures audio transcription rather than NLG text creation.
  • Computer Vision Performance Measure, which evaluates visual processing rather than NLG linguistic output.
• See: Natural Language Generation Task, NLG System, Text Generation, Machine Translation, Text Summarization, Dialogue System, Language Model, NLU Performance Measure, Controlled Natural Language Generation, Neural Text Generation.

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