What is METEOR score?

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The METEOR score (Metric for Evaluation of Translation with Explicit ORdering) is an evaluation metric used to measure the quality of machine translation and other natural language generation tasks (like summarization). It was designed as an improvement over BLEU, addressing some of its weaknesses.

🔹 Key Idea

METEOR evaluates how well a machine-generated sentence matches a human reference sentence by looking beyond exact word matches. It includes:

• Exact word matches (same words).

• Stemming matches (e.g., run vs. running).

• Synonym matches (e.g., big vs. large).

• Paraphrase matches (different words/phrases but same meaning).

🔹 How It Works

1. Align words between the generated and reference sentence using rules (exact, stem, synonym, paraphrase).

2. Compute:

   • Precision = fraction of matched words out of generated words.

   • Recall = fraction of matched words out of reference words.

3. Combine them into an F-score (with recall usually given more weight than precision).

4. Apply a fragmentation penalty for disordered word sequences (to reward correct word order).

🔹 Why It’s Useful

• Unlike BLEU (which relies heavily on n-gram overlap), METEOR captures meaning and linguistic similarity.

• It correlates better with human judgment of translation quality.

• It balances precision, recall, synonyms, and order, making it more robust.

🔹 Example

• Reference: “The boy is playing football.”

• Generated: “A kid plays soccer.”

  • BLEU may give a low score (few exact n-gram matches).

  • METEOR scores higher because boy ↔ kid, football ↔ soccer, playing ↔ plays are valid matches.

👉 In short: The METEOR score evaluates translation and text generation by aligning words using exact, stem, synonym, and paraphrase matches, balancing precision & recall, and considering word order — making it closer to human evaluation.

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