Evaluating Natural Language Processing (NLP) Models: An In-depth Look at BLEU, ROUGE, and F1 Score

  • Natural Language Processing has revolutionized the way we interact with machines and interpret textual data. Models in NLP are designed to understand, interpret, and generate human-like text, making them integral in fields like text analysis, translation, summarization, and information extraction.
  • Evaluating the performance of these models is crucial for their development and improvement. In this regard, several metrics have been proposed, such as BLEU, ROUGE, and the F1 score, each of which serves different purposes and has its own strengths and weaknesses.
  • This article will explore three widely used evaluation metrics in NLP: BLEU, ROUGE, and F1 score.

BLEU (Bilingual Evaluation Understudy)

  • BLEU, an acronym for Bilingual Evaluation Understudy, is predominantly used in machine translation. It quantifies the quality of the machine-generated text by comparing it with a set of reference translations. The crux of the BLEU score calculation is the precision of n-grams (continuous sequence of n items in text) in the machine-translated text. However, to prevent the overestimation of precision due to shorter sentences, BLEU includes a brevity penalty factor. Despite its widespread use, it’s important to note that BLEU mainly focuses on precision, and lacks a recall component.
  • Mathematically, precision for unigram (single word) is calculated as follows:
  • Precision = (Number of correct words in machine translation) / (Total words in machine translation)
  • BLEU extends this idea to consider precision of n-grams (continuous sequence of n items from a given sample of text). However, BLEU uses a modified precision calculation to avoid the problem of artificially inflated precision scores.
  • The equation of BLEU score for n-grams is:

  • \[BLEU = BP * exp ( Sum (from i=1 to n) w_i * log (p_i) )\]


  • BP is the brevity penalty (to penalize short sentences)
  • w_i are the weights for each gram (usually, we give equal weight)
  • p_i is the precision for each i-gram

ROUGE (Recall-Oriented Understudy for Gisting Evaluation)

  • ROUGE, which stands for Recall-Oriented Understudy for Gisting Evaluation, is used primarily for evaluating automatic summarization and, sometimes, machine translation. The key feature of ROUGE is its focus on recall, measuring how many of the reference n-grams are found in the system-generated summary. This makes it especially useful for tasks where coverage of key points is important. Among its variants, ROUGE-N computes the overlap of n-grams, ROUGE-L uses the longest common subsequence to account for sentence-level structure similarity, and ROUGE-S includes skip-bigram statistics.
  • ROUGE-N specifically refers to the overlap of N-grams between the system and reference summaries.

  • \[ROUGE-N = (Number of N-grams in both system and reference summary) / (Total number of N-grams in reference summary)\]
  • ROUGE-L considers sentence level structure similarity naturally and identifies longest co-occurring in-sequence n-grams automatically.
  • ROUGE-S includes skip-bigram plus unigram-based co-occurrence statistics. Skip-bigram is any pair of words in their sentence order.

F1 Score

  • The F1 score, commonly used in many machine learning classification problems, is also applicable to various NLP tasks like Named Entity Recognition, POS-tagging, etc. The F1 score is the harmonic mean of precision and recall, and thus balances the two and prevents extreme cases where one is favored over the other. It ranges from 0 to 1, where 1 signifies perfect precision and recall.
  • Precision is the number of true positive results divided by the number of all positive results, including those not identified correctly. Recall, on the other hand, is the number of true positive results divided by the number of all samples that should have been identified as positive.
  • The F1 score is the harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0.
  • The mathematical representation is as follows:
  • \[F1 Score = 2 * (Precision * Recall) / (Precision + Recall)\]
  • In NLP, F1 score is often used in tasks like Named Entity Recognition, POS-tagging, etc.