METEOR **(Metric for Evaluation of Translation with Explicit ORdering)**is a metric used to evaluate the quality of machine-generated text, such as translations or summaries, by comparing them to human reference outputs. It improves upon traditional metrics like BLEU by incorporating semantic matching and focusing on linguistic properties like synonyms, stemming, and word order.
Key Characteristics:
- Semantic Matching: Goes beyond exact word matching by recognizing synonyms, stemming variations (e.g., “run” and “running”), and paraphrases.
- Precision and Recall: Balances precision (correct words in the output) and recall (all necessary words included), giving equal weight to both.
- Word Alignment: Measures the alignment between the machine-generated text and the reference text to capture similarities.
- Order Sensitivity: Accounts for word order to ensure outputs are both accurate and coherent.
Applications:
- Machine Translation: Evaluates the quality of translations compared to human-provided references.
- Text Summarization: Measures how well a generated summary aligns with a human-crafted summary.
- Paraphrase Generation: Assesses the similarity and relevance of machine-generated paraphrases.
- NLP Research: Benchmarks language models in various natural language processing tasks.
Why It Matters:
METEOR provides a more nuanced evaluation of generated text by incorporating linguistic properties, making it particularly useful for assessing models designed for tasks requiring semantic understanding and fluency.
