Intrinsic Natural Language Generation (NLG) Performance Measure

An Intrinsic Natural Language Generation (NLG) Performance Measure is an NLG Performance Measure that evaluates generated text quality independently of downstream task performance.

• AKA: Internal NLG Quality Measure, Text-Focused NLG Evaluation Metric, Intrinsic Text Generation Measure.
• Context:
  • It can typically assess NLG Grammatical Correctness through NLG syntax evaluation.
  • It can typically measure NLG Lexical Diversity through NLG vocabulary richness analysis.
  • It can typically evaluate NLG Text Coherence through NLG discourse structure assessment.
  • It can typically quantify NLG Style Consistency through NLG stylistic feature analysis.
  • It can typically determine NLG Fluency through NLG readability scoring.
  • It can typically establish NLG Evaluation Reliability Ceilings through NLG inter-expert agreement measures like Krippendorff's Alpha or Cohen's Kappa.
  • It can often measure NLG Semantic Adequacy without NLG task-specific context.
  • It can often evaluate NLG Content Coverage through NLG information completeness metrics.
  • It can often assess NLG Linguistic Quality using NLG language model scoring.
  • It can often quantify NLG Text Naturalness through NLG human-likeness evaluation.
  • It can often determine NLG Factual Consistency through NLG internal contradiction detection.
  • It can often decompose into NLG Semantic Accuracy Measures and NLG Stylistic Fluency Measures following MQM (Multidimensional Quality Metrics) Framework.
  • It can often require NLG Statistical Validation through stratified bootstrap tests or paired t-tests when comparing NLG system performance.
  • It can range from being a Human-Based Intrinsic NLG Measure to being an Automated Intrinsic NLG Measure, depending on its NLG evaluation method.
  • It can range from being a Syntax-Based Intrinsic NLG Measure to being a Semantics-Based Intrinsic NLG Measure, depending on its NLG linguistic level.
  • It can range from being a Reference-Based Intrinsic NLG Measure to being a Reference-Free Intrinsic NLG Measure, depending on its NLG comparison approach.
  • It can range from being a Single-Aspect Intrinsic NLG Measure to being a Multi-Aspect Intrinsic NLG Measure, depending on its NLG evaluation scope.
  • It can range from being a Surface-Level Intrinsic NLG Measure to being a Deep-Level Intrinsic NLG Measure, depending on its NLG analysis depth.
  • It can support NLG System Development through NLG quality feedback.
  • It can enable NLG model comparison without NLG application deployment.
  • ...
• Examples:
  • Automated Intrinsic NLG Measures, such as:
    • N-gram-Based Intrinsic NLG Measures, such as:
      • BLEU Score for NLG precision evaluation.
      • ROUGE Score for NLG recall evaluation.
      • METEOR Score for NLG flexible matching.
      • CIDEr Score for NLG consensus evaluation.
    • Embedding-Based Intrinsic NLG Measures, such as:
      • BERTScore for NLG contextual similarity.
      • MoverScore for NLG semantic distance.
      • BLEURT for NLG learned quality estimation.
    • Language Model Intrinsic NLG Measures, such as:
      • Perplexity for NLG probabilistic evaluation.
      • GPTScore for NLG generative model assessment.
      • BARTScore for NLG bidirectional evaluation.
  • Human-Based Intrinsic NLG Measures, such as:
    • NLG Fluency Rating for NLG grammatical assessment.
    • NLG Coherence Rating for NLG logical flow evaluation.
    • NLG Adequacy Rating for NLG content completeness.
    • NLG Style Rating for NLG appropriateness assessment.
  • Pairwise Preference Intrinsic NLG Measures, such as:
    • Bradley-Terry Model Score for NLG system ranking estimation.
    • Human Parity Index (HPI) for NLG expert-comparison evaluation.
    • Win-Rate Analysis for NLG model superiority assessment.
    • Thurstone Model Score for NLG pairwise ability estimation.
  • Linguistic Feature Intrinsic NLG Measures, such as:
    • NLG Lexical Diversity Measures, such as:
      • Type-Token Ratio (TTR) for NLG vocabulary variety.
      • MTLD (Measure of Textual Lexical Diversity) for NLG lexical richness.
    • NLG Syntactic Complexity Measures, such as:
      • Parse Tree Depth for NLG structural complexity.
      • Dependency Distance for NLG syntactic sophistication.
    • NLG Readability Measures, such as:
      • Flesch Reading Ease for NLG comprehension difficulty.
      • Gunning Fog Index for NLG text complexity.
  • Coherence-Based Intrinsic NLG Measures, such as:
    • NLG Entity Grid Model for NLG entity transition patterns.
    • NLG Lexical Chain Analysis for NLG thematic continuity.
    • NLG RST (Rhetorical Structure Theory) Score for NLG discourse relations.
  • Error-Based Intrinsic NLG Measures, such as:
    • NLG Grammatical Error Rate for NLG syntax accuracy.
    • NLG Repetition Detection Score for NLG redundancy assessment.
    • NLG Contradiction Detection Score for NLG logical consistency.
  • Ceiling-Normalized Intrinsic NLG Measures, such as:
    • Percent-of-Ceiling (PoC) Score for NLG human-normalized performance.
    • Effect Size Gap Measure using Hedges' g for NLG rating comparison.
    • Reliability-Adjusted NLG Score for NLG ceiling-aware evaluation.
  • ...
• Counter-Examples:
  • Extrinsic NLG Performance Measure, which evaluates generated text impact on downstream task performance.
  • Task-Specific NLG Performance Measure, which assesses generated text effectiveness for specific applications.
  • NLG User Engagement Metric, which measures NLG content interaction rather than intrinsic text quality.
  • NLG SEO Performance Measure, which evaluates search ranking effectiveness rather than NLG linguistic quality.
  • NLG Business Impact Metric, which assesses commercial value rather than NLG text quality.
• See: Natural Language Generation, NLG Performance Measure, Extrinsic NLG Performance Measure, ROUGE, BLEU, METEOR, BERTScore, Human Evaluation, Automated Text Evaluation, Text Quality Assessment, Coh-Metrix, Linguistic Feature Analysis, MQM Framework, Bradley-Terry Model, Human Parity Index.

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References

2011

• (Crossley & McNamara, 2011) ⇒ Scott A. Crossley, and Danielle S. McNamara. (2011). “Understanding Expert Ratings of Essay Quality: Coh-Metrix Analyses of First and Second Language Writing.” International Journal of Continuing Engineering Education and Life Long Learning, 21(2-3).
  • ABSTRACT: This article reviews recent studies in which human judgements of essay quality are assessed using Coh-Metrix, an automated text analysis tool. The goal of these studies is to better understand the relationship between linguistic features of essays and human judgements of writing quality. Coh-Metrix reports on a wide range of linguistic features, affording analyses of writing at various levels of text structure, including surface, text-base, and situation model levels. Recent studies have examined linguistic features of essay quality related to co-reference, connectives, syntactic complexity, lexical diversity, spatiality, temporality, and lexical characteristics. These studies have analysed essays written by both first language and second language writers. The results support the notion that human judgements of essay quality are best predicted by linguistic indices that correlate with measures of language sophistication such as lexical diversity, word frequency, and syntactic complexity. In contrast, human judgements of essay quality are not strongly predicted by linguistic indices related to cohesion. Overall, the studies portray high quality writing as containing more complex language that may not facilitate text comprehension.

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