BLEU Score

Introduction

The BLEU (Bilingual Evaluation Understudy) score is a metric used to evaluate the quality of text which has been machine-translated from one language to another. It was introduced by Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu in 2002. The BLEU score is one of the most widely used metrics for evaluating the performance of machine translation systems. It is based on the comparison of machine-generated translations to one or more reference translations created by humans.

Background

The BLEU score was developed to provide an automatic, quantitative measure of the quality of machine translation. Prior to its introduction, the evaluation of machine translation systems was largely subjective and required human judges to assess the quality of translations. This process was time-consuming, expensive, and inconsistent. The BLEU score aimed to provide a more objective and reproducible method for evaluating machine translation systems.

Calculation of BLEU Score

The BLEU score is calculated by comparing n-grams of the candidate translation to n-grams of the reference translations. An n-gram is a contiguous sequence of n items from a given sample of text. The BLEU score uses precision to measure the overlap between the candidate and reference translations. Precision is calculated as the ratio of the number of matching n-grams to the total number of n-grams in the candidate translation.

Modified Precision

One of the key innovations of the BLEU score is the use of modified precision. Modified precision accounts for the fact that a candidate translation may contain repeated n-grams that do not appear in the reference translations. To address this, the BLEU score limits the number of times an n-gram can be counted in the candidate translation to the maximum number of times it appears in any of the reference translations.

Brevity Penalty

The BLEU score also includes a brevity penalty to discourage overly short translations. The brevity penalty is calculated based on the length of the candidate translation relative to the length of the reference translations. If the candidate translation is shorter than the reference translations, the brevity penalty reduces the BLEU score. If the candidate translation is longer or equal in length to the reference translations, the brevity penalty is set to 1 and does not affect the BLEU score.

Final BLEU Score Calculation

The final BLEU score is calculated as the geometric mean of the modified precisions for different n-gram lengths, multiplied by the brevity penalty. The BLEU score ranges from 0 to 1, with higher scores indicating better translation quality.

Strengths and Limitations

The BLEU score has several strengths that have contributed to its widespread adoption. It is easy to calculate, reproducible, and provides a single numerical score that can be used to compare different machine translation systems. Additionally, the BLEU score can be calculated quickly, making it suitable for use in large-scale evaluations.

However, the BLEU score also has several limitations. It relies on n-gram overlap, which means that it may not capture the semantic meaning of the translation. As a result, translations that are semantically accurate but use different wording from the reference translations may receive low BLEU scores. Additionally, the BLEU score does not account for the fluency or grammatical correctness of the translation.

Applications

The BLEU score is widely used in the field of natural language processing (NLP) for evaluating machine translation systems. It is also used in other areas of NLP, such as text summarization and paraphrase detection, where the goal is to generate text that is similar to a reference text.

Alternatives to BLEU Score

Several alternative metrics have been proposed to address the limitations of the BLEU score. These include:

**METEOR**: The METEOR (Metric for Evaluation of Translation with Explicit ORdering) score is another metric used to evaluate machine translation quality. It addresses some of the limitations of the BLEU score by considering synonyms, stemming, and paraphrasing.

**ROUGE**: The ROUGE (Recall-Oriented Understudy for Gisting Evaluation) score is commonly used for evaluating text summarization systems. It measures the overlap of n-grams, word sequences, and word pairs between the candidate and reference summaries.

**TER**: The Translation Edit Rate (TER) measures the number of edits required to change a candidate translation into one of the reference translations. Edits include insertions, deletions, substitutions, and shifts of word sequences.

Future Directions

The field of machine translation is rapidly evolving, and new evaluation metrics are continually being developed. Researchers are exploring ways to incorporate more sophisticated linguistic and semantic information into evaluation metrics. Additionally, there is ongoing work to develop metrics that better correlate with human judgments of translation quality.

References