BLEU
Introduction
BLEU, or Bilingual Evaluation Understudy, is a metric for evaluating the quality of text which has been machine-translated from one language to another. It was introduced in 2002 by Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu at IBM. BLEU is one of the most widely used metrics for assessing the performance of machine translation systems. It provides a quantitative measure of how closely a machine-generated translation matches a set of reference translations created by humans.
Background
The development of BLEU was motivated by the need for an automatic, objective, and quick method to evaluate the quality of machine translations. Prior to BLEU, the assessment of translation quality was predominantly subjective, relying on human judgment, which is time-consuming and expensive. BLEU introduced a standardized approach that allows for consistent and reproducible evaluation.
Methodology
BLEU evaluates translation quality by comparing the n-grams of the candidate translation to those of the reference translations. An n-gram is a contiguous sequence of n items from a given sample of text. BLEU calculates precision by determining how many n-grams in the candidate translation appear in any of the reference translations. The precision scores for different n-gram lengths are then combined using a geometric mean, which is subsequently multiplied by a brevity penalty to account for differences in length between the candidate and reference translations.
N-gram Precision
The n-gram precision is calculated by counting the number of n-grams in the candidate translation that match n-grams in the reference translations, divided by the total number of n-grams in the candidate translation. BLEU typically uses n-grams of lengths 1 to 4. This approach ensures that both individual word choices and the order of words are considered in the evaluation.
Brevity Penalty
The brevity penalty is applied to prevent the system from favoring shorter translations that might achieve high precision by omitting words. It is calculated based on the ratio of the length of the candidate translation to the length of the reference translation. If the candidate translation is shorter than the reference, the brevity penalty reduces the BLEU score.
Calculation of BLEU Score
The BLEU score is computed as follows:
1. Calculate the precision for each n-gram length. 2. Compute the geometric mean of these precision scores. 3. Apply the brevity penalty. 4. The final BLEU score is the product of the geometric mean and the brevity penalty.
The BLEU score ranges from 0 to 1, with 1 indicating a perfect match with the reference translations. In practice, BLEU scores are often expressed as a percentage.
Limitations and Criticisms
Despite its widespread use, BLEU has several limitations:
- **Lack of Semantic Understanding:** BLEU evaluates translations based on surface-level n-gram matches and does not account for semantic equivalence. As a result, it may not accurately reflect the quality of translations that use different wording but convey the same meaning.
- **Sensitivity to Reference Translations:** The BLEU score is highly dependent on the quality and number of reference translations. A limited set of reference translations may not capture all valid ways of expressing the same content, leading to lower BLEU scores for valid translations.
- **Length Bias:** Although the brevity penalty addresses length discrepancies, BLEU may still favor translations that are either too short or too long, depending on the specific implementation.
- **Lack of Contextual Evaluation:** BLEU does not consider the broader context of the text, which can be crucial for accurate translation, especially in languages with complex grammatical structures.
Applications
BLEU is primarily used in the field of machine translation but has also been applied to other areas of natural language processing (NLP), such as text summarization and speech recognition. It serves as a benchmark for comparing different translation models and systems, enabling researchers and developers to track progress and improvements over time.
Alternatives to BLEU
Several alternative metrics have been proposed to address the limitations of BLEU:
- **METEOR:** This metric considers synonyms, stemming, and paraphrasing, providing a more nuanced evaluation of translation quality.
- **ROUGE:** Originally developed for text summarization, ROUGE measures recall rather than precision, focusing on the overlap of n-grams between candidate and reference texts.
- **TER (Translation Edit Rate):** TER calculates the number of edits required to change a candidate translation into one of the reference translations, providing a measure of translation effort.
Conclusion
BLEU remains a cornerstone in the evaluation of machine translation systems, offering a fast and automated way to assess translation quality. While it has its limitations, BLEU's simplicity and ease of use have contributed to its enduring popularity. As the field of NLP continues to evolve, researchers are exploring new metrics and methods to complement and enhance BLEU's capabilities.