Question-Answering (QA) Performance Measure

A Question-Answering (QA) Performance Measure is an NLP performance measure that evaluates QA system effectiveness for question-answering (QA) tasks.

• AKA: QA Evaluation Metric, QA Quality Measure, Question-Answering Assessment Metric, QA Scoring Method.
• Context:
  • It can typically measure QA Answer Accuracy through QA exact match scoring.
  • It can typically evaluate QA Answer Relevance using QA semantic similarity metrics.
  • It can typically assess QA Answer Completeness via QA coverage evaluation.
  • It can typically quantify QA Answer Correctness through QA factuality checking.
  • It can typically determine QA Answer Fluency using QA readability assessment.
  • It can often measure QA Answer Precision through QA token-level matching.
  • It can often evaluate QA Answer Recall using QA information retrieval metrics.
  • It can often assess QA Answer Consistency via QA contradiction detection.
  • It can often quantify QA Answer Confidence through QA uncertainty estimation.
  • It can often determine QA Response Time using QA latency measurement.
  • It can range from being an Extractive QA Performance Measure to being an Abstractive QA Performance Measure, depending on its QA answer generation type.
  • It can range from being a Single-Answer QA Performance Measure to being a Multi-Answer QA Performance Measure, depending on its QA answer cardinality.
  • It can range from being a Factoid QA Performance Measure to being a Complex QA Performance Measure, depending on its QA question complexity.
  • It can range from being a Closed-Domain QA Performance Measure to being an Open-Domain QA Performance Measure, depending on its QA knowledge scope.
  • It can range from being a Reference-Based QA Performance Measure to being a Reference-Free QA Performance Measure, depending on its QA evaluation approach.
  • It can integrate with QA Benchmark Datasets for QA standardized evaluation.
  • It can support QA System Comparisons through QA normalized scoring.
  • ...
• Examples:
  • Core QA Performance Measures, such as:
    • QA Exact Match (EM) Score for QA precise answer matching.
    • QA F1 Score for QA token-level overlap evaluation.
    • QA BLEU Score for QA n-gram precision assessment.
    • QA ROUGE Score for QA answer coverage evaluation.
    • QA Mean Reciprocal Rank (MRR) for QA ranking effectiveness.
  • Task-Specific QA Performance Measures, such as:
    • Reading Comprehension QA Measures, such as:
      • SQuAD Evaluation Metric for QA extractive answer assessment.
      • MS MARCO Evaluation Score for QA passage retrieval evaluation.
      • Natural Questions (NQ) Score for QA long-form answer evaluation.
    • Conversational QA Measures, such as:
      • CoQA Evaluation Metric for QA dialogue coherence assessment.
      • QuAC Score for QA information-seeking dialogue evaluation.
    • Knowledge-Based QA Measures, such as:
      • TriviaQA Score for QA factual knowledge evaluation.
      • HotpotQA Metric for QA multi-hop reasoning assessment.
    • Visual QA Measures, such as:
      • VQA Accuracy Score for QA image-based answer evaluation.
      • GQA Metric for QA compositional reasoning assessment.
  • Semantic QA Performance Measures, such as:
    • BERTScore for QA for QA contextual embedding evaluation.
    • BLEURT for QA for QA learned metric evaluation.
    • MoverScore for QA for QA semantic alignment assessment.
    • Answer Equivalence Score for QA paraphrase detection.
  • Domain-Specific QA Performance Measures, such as:
    • Medical QA Performance Measures, such as:
      • MedQA Score for QA clinical knowledge evaluation.
      • PubMedQA Metric for QA biomedical literature assessment.
    • Legal QA Performance Measures, such as:
      • CaseHOLD Score for QA legal reasoning evaluation.
      • Contract QA Metric for QA legal document understanding.
    • Scientific QA Performance Measures, such as:
      • ScienceQA Score for QA scientific reasoning evaluation.
      • AI2 Reasoning Challenge (ARC) Score for QA scientific knowledge assessment.
  • Robustness QA Performance Measures, such as:
    • Adversarial QA Score for QA robustness evaluation.
    • DROP Metric for QA numerical reasoning assessment.
    • DuoRC Score for QA paraphrased question handling.
  • Multi-Modal QA Performance Measures, such as:
    • MMQA Score for QA cross-modal understanding.
    • OK-VQA Metric for QA external knowledge integration.
  • ...
• Counter-Examples:
  • Text Summarization Performance Measure, which evaluates summary quality rather than QA answer accuracy.
  • Machine Translation Performance Measure, which assesses translation quality rather than QA response correctness.
  • Information Extraction Performance Measure, which measures extraction precision rather than QA answer relevance.
  • Text Classification Performance Measure, which evaluates classification accuracy rather than QA answer completeness.
  • Named Entity Recognition Performance Measure, which assesses entity detection rather than QA answer quality.
• See: Question-Answering (QA) Task, QA System, QA Benchmark, Natural Language Generation (NLG) Performance Measure, Summarization Performance Measure, Reading Comprehension Task, Information Retrieval Performance Measure, Dialogue System Evaluation, SQuAD Dataset, MS MARCO, Natural Questions Dataset.

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