NLP Model Evaluation: Understanding BLEU, ROUGE, METEOR, and BERTScore

Evaluating Natural Language Processing (NLP) models is crucial for assessing their effectiveness, usability, and reliability in real-world applications. Metrics such as BLEU, ROUGE, METEOR, and BERTScore play a pivotal role in this process by providing quantitative measures of a model’s performance. These metrics help in understanding how well a model can translate, summarize, generate, or understand text in comparison to human standards or reference materials. Evaluation is vital not only for fine-tuning and improving models but also for ensuring they meet the necessary standards for deployment in sensitive applications like medical diagnosis, legal analysis, or customer service automation.

However, evaluating NLP models presents several challenges. The complexities of language, including contextual details, idiomatic expressions, and cultural allusions, present challenges for models to accurately interpret and for evaluation metrics to precisely measure. Many conventional metrics focus on surface-level text features like word overlap, which may not fully represent the model’s ability to understand or generate semantically and syntactically correct language. Additionally, the reliance on reference datasets for evaluation can introduce biases or limit the scope of assessment, as these datasets may not encompass the diversity of real-world language use.

In this article, we will briefly explore four key metrics: BLEU, ROUGE, METEOR, and BERTScore. Our discussion will focus on their applications, as well as their respective strengths and weaknesses.

ROUGE (Recall-Oriented Understudy for Gisting Evaluation):

It is a set of metrics used for evaluating automatic summarization and machine translation. It compares an automatically produced summary or translation against a set of reference summaries (usually human-written). ROUGE measures the quality of the summary by counting the number of overlapping units such as n-grams, word sequences, and word pairs between the model-generated text and the reference texts. The most common variants of ROUGE are:

ROUGE-N: Focuses on n-grams (N-word phrases). ROUGE-1 and ROUGE-2 (unigrams and bigrams, respectively) are most common.

ROUGE-L: Based on the Longest Common Subsequence (LCS), which takes into account sentence level structure similarity naturally and identifies longest co-occurring in-sequence n-grams automatically.

ROUGE typically reports three metrics.

Precision: The proportion of the n-grams in the model generated summary that are also found in the reference summary.

Recall: The proportion of the n-grams in the reference summary that are also found in the model generated summary.

F-Score (F1 Score): The harmonic mean of precision and recall, balancing the two.

ROUGE scores range from 0 to 1, where 0 indicates no overlap between the machine-generated texts and the reference texts. 1 indicates a perfect match with the reference texts.

Advantages

1. Simple and Easy to Use: ROUGE is straightforward to implement and understand, making it popular for summarization tasks.
2. Automatic Evaluation: It enables quick and automatic evaluation, which is essential for large-scale or ongoing projects.
3. Quantitative Measurement: Offers a clear, quantitative measure of performance, facilitating comparisons between different models or approaches.

Disadvantages

1. Limited Contextual Understanding: ROUGE primarily focuses on surface-level text features and may not fully capture the semantic accuracy or coherence of the generated text.
2. Reference Dependence: Its effectiveness can depend heavily on the quality and representativeness of the reference summaries.

BLEU (Bilingual Evaluation Understudy)

The BLEU score is a widely used metric for evaluating the quality of machine-translated text (Candidate) against reference translations (Reference). Developed by IBM researchers, BLEU assesses translation accuracy by measuring the overlap of n-grams between the machine-generated text and a set of high-quality reference translations. It primarily focuses on precision. BLEU is renowned for its simplicity and effectiveness, making it a standard benchmark in the field of machine translation. However, it primarily evaluates surface-level lexical similarities, often overlooking deeper semantic and contextual nuances of language.

Candidate: This is the output from our translation system that we want to evaluate.

Reference: These are high-quality translations (typically done by humans) that we compare the candidate text against. There can be more than one reference translation for robustness.

Calculation

Split the candidate and reference translations into words (tokens). Tokenization should be consistent across both sets of texts.

Calculate n-gram Precision (P)

• For each n-gram length (typically from 1 to 4):
• Count the number of n-grams in the candidate that appear in the reference : number of common n-gram in both referece and candidate.
• Divide this number by the total number of n-grams in the candidate translation to get the precision for each n-gram length.

Breach Penalty (BP)

• If the candidate translation is shorter than the reference translation(s), we should apply a penalty to prevent favoring overly short translations.
• BP formula: BP=exp(1−r/c) if c < r, else BP = 1
• Where c is the length of the candidate translation and r is the effective reference length.

BLEU Score

• The BLEU score is calculated using a geometric mean of the n-gram precisions, multiplied by the brevity penalty.
• BLEU Score = BP*exp((1/n)*∑log⁡(pi)), here n goes from 1 to 4 (n-grams)
• Where pi​ is the precision for n-grams.

The range of BLEU score: It typically from 0 to 1, where 0 indicates no overlap between the translated text and the reference translations, representing the lowest possible score and suggesting very poor translation quality. 1 indicates a perfect match with the reference translations, representing the highest possible score and suggesting an ideal translation quality.

Advantages

1. Objective Evaluation: Provides a numerical score for easy and objective comparison of translation models.
2. Broad Applicability: Language-independent, making it suitable for evaluating translations between any language pair.
3. Efficiency: Automated and fast, facilitating large-scale and rapid assessments compared to manual evaluation methods.

Disadvantages

1. Limited Semantic Assessment: BLEU focuses on literal word overlap, not capturing the full meaning or context of translations.
2. Dependence on Reference Quality: The accuracy of scores relies heavily on the quality of reference translations.
3. Inadequate at Sentence Level: More reliable for evaluating large corpora than individual sentences, possibly overlooking nuances in shorter texts.

METEOR (Metric for Evaluation of Translation with Explicit Ordering)

It is an advanced metric for evaluating machine translation that was developed to address some limitations of the BLEU score. Unlike BLEU, METEOR not only considers exact word matches but also incorporates stemming and synonyms to evaluate translations, thereby capturing a broader range of linguistic similarities. It uniquely balances precision and recall in its assessment and introduces a penalty for word order differences to evaluate the fluency of translations. METEOR is known for its higher correlation with human judgment, especially at the sentence level, making it a nuanced and comprehensive metric for translation quality evaluation. However, its sophistication also means it is more computationally intensive than simpler metrics like BLEU.

Alignment-Based: METEOR creates alignments between the words in the candidate translation and the reference translation, focusing on exact, stem, synonym, and paraphrase matches.

Recall and Precision: Unlike BLEU, which only considers precision, METEOR calculates both precision and recall. This dual focus helps balance the assessment.

Harmonic Mean: METEOR uses the harmonic mean of recall and precision, with a higher emphasis on recall (the modified version of the harmonic mean to give more importance to recall than precision). This differs from BLEU, which uses a modified form of precision.

Penalty for Word Order Differences: METEOR includes a penalty for incorrect word order, which makes it sensitive to the fluency of the translation.

Language-Independent: While initially developed for English, METEOR has been extended to support multiple languages with language-specific parameters and resources.

Calculation

Calculate Matches

Count the number of unigrams in the candidate that exactly match the unigrams in the reference.

Calculate Precision and Recall

• Precision (P): The proportion of unigrams in the candidate translation that appear in the reference translation.
• Recall (R): The proportion of unigrams in the reference translation that appear in the candidate translation.

Calculate the Harmonic Mean of Precision and Recall

The F-mean is calculated as: F-mean=10*P*R/(R+9*P).​ This places more weight on recall than precision.

Penalty for Word Order

A penalty is applied for differences in word order. The penalty is calculated as: Penalty=0.5⋅(# of chunks/# of matches)**3; where a “chunk” is a set of adjacent words in the candidate that are in the same order as in the reference.

Final METEOR Score

The final score is computed as: Score=(1−Penalty)*F-mean​

Refrences: These refrences discuss this metric more throughly: Ref1, Ref2, Ref3.

Advantages

Recall-Oriented: METEOR balances precision and recall, unlike BLEU, which focuses only on precision. This makes METEOR more sensitive to whether the translation covers all aspects of the reference.

Word Order Consideration: METEOR includes a penalty for incorrect word order, making it more sensitive to the fluency and grammatical correctness of the translation.

Flexible Matching: It uses various forms of word matching, including exact, stem, synonym, and paraphrase matching, leading to a more nuanced evaluation of translation quality.

Higher Correlation with Human Judgment: METEOR often correlates better with human evaluation, particularly at the sentence level, due to its comprehensive approach to matching and its focus on recall.

Language Adaptability: METEOR can be adapted to different languages with appropriate tuning of its parameters and the use of language-specific resources.

Disadvantages

Complexity: METEOR is more complex to compute than BLEU due to its sophisticated matching criteria and the incorporation of recall, penalization for word order differences, and paraphrasing.

Dependency on Language-Specific Resources: For optimal performance, METEOR requires language-specific resources like stemmers and synonym dictionaries, which may not be available for all languages.

Less Standardized in the Field: Despite its advantages, METEOR is not as widely used as BLEU in machine translation research, potentially due to its complexity and the dominance of BLEU as a standard metric.

Computationally More Intensive: The advanced matching techniques and the calculation of recall and word order penalties make METEOR more computationally intensive compared to simpler metrics like BLEU.

BERT (Bidirectional Encoder Representations from Transformers)

BERTScore is a novel metric for evaluating text generation tasks such as machine translation, text summarization, and image captioning, leveraging the advancements in deep learning language models. It utilizes the BERT model to generate contextual embeddings for tokens in both the candidate and reference texts. BERTScore then calculates the cosine similarity between these embeddings, capturing the semantic similarity between words beyond mere lexical matching. This approach allows it to assess the quality of text generation with a focus on semantic content and context, making it more sensitive to the meanings conveyed in the text. While BERTScore offers a more nuanced assessment compared to traditional overlap-based metrics, it is computationally intensive, requiring significant resources due to its reliance on large, pre-trained language models.

Calculation

Contextual Embedding

BERTScore uses BERT or other transformer-based models to obtain contextual embeddings for each token in both the candidate (generated) text and the reference text.

Cosine Similarity Calculation

It computes the cosine similarity between each token in the candidate text and each token in the reference text. This process captures the semantic similarity rather than just lexical matching.

Scoring

For each token in the candidate text, BERTScore selects the maximum similarity score with tokens in the reference text. It then averages these scores to compute the final precision score. A similar process is applied for recall (considering each token in the reference text), and an F1 score is calculated from the precision and recall.

!pip install torch torchvision torchaudio
!pip install bert-score

import torch
from bert_score import score

candidate = ['The quick brown dog jumps over the lazy fox.']
reference = ['The quick brown fox jumps over the lazy dog.']
P, R, F1 = score(candidate, reference, lang='en')
print(P, R, F1) # Precission, Recall and F1 score

Advantages

1. Semantic Evaluation: BERTScore evaluates the semantic content, which is more robust than surface-level overlap.
2. Robustness to Paraphrasing: It can recognize paraphrased content as similar, which traditional metrics might miss.
3. Language Model Flexibility: It can leverage different language models.

Disadvantages

1. Computational Intensity: Requires significant computational resources to generate embeddings.
2. Model Dependency: The quality of the evaluation is dependent on the underlying language model.
3. Less Interpretability: Scores can be less interpretable compared to simpler metrics like BLEU.

Brief Comparison:

ROUGE: Strength: ROUGE is effective for evaluating text summarization, especially in capturing content overlap between generated summaries and references. Weakness: It struggles with understanding the context and semantics, often overlooking the coherence and quality of content.

BLEU: Strength: BLEU is renowned for its simplicity and efficiency in evaluating machine translation, providing quick, quantitative comparisons. Weakness: It lacks the ability to assess semantic accuracy and can miss nuances in translation due to its focus on word overlap.

METEOR: Strength: METEOR excels in balancing precision and recall, and it accounts for synonyms and paraphrasing, aligning closer with human judgment. Weakness: It is computationally more complex and requires extensive linguistic resources, limiting its versatility across languages.

BERTScore: Strength: BERTScore leverages contextual embeddings for semantic evaluation, making it adept at understanding nuanced meanings beyond mere word matches. Weakness: It is computationally intensive and heavily dependent on the underlying language model, impacting its accessibility and performance.