Hello from Bering Lab! Today, we dive into the critical metric for evaluating translation quality: the BLEU score. Legal translations, as you know, require 100% precision. A single mistranslated word in a contract can cause massive financial losses, and a small error in a patent document can jeopardize years of research and innovation.
In such a meticulous field, the BLEU score serves as a benchmark for translation accuracy. It quantifies how close AI translations are to human translations, offering an objective measure of quality. Let’s unpack what the BLEU score is, why it matters, and where its limitations lie.
✍🏻 What is the BLEU Score?
BLEU (Bilingual Evaluation Understudy) score evaluates how “human-like” a machine translation is. Simply put, it measures the overlap between machine-translated text and human reference translations on a scale of 0 to 1, where 1 indicates perfect alignment.
The BLEU score uses an approach called n-gram matching. Here’s how it works:
- N-grams refer to consecutive word sequences. For instance, in “I ate an apple,” the 2-grams are “I ate” and “ate an apple.”
- BLEU calculates how many n-grams in the machine translation match those in the reference translation, ensuring precision.
But what if a translation is too short? For instance, if the machine outputs just “apple,” it would score high for matching a single word but fail to convey the original meaning. To address this, BLEU incorporates a Brevity Penalty that penalizes overly short translations.
✍🏻 Limitations of the BLEU Score
While BLEU is widely used, it has notable limitations:
- Lack of Synonym Recognition:
Language is flexible. A phrase like “It’s raining cats and dogs” can be translated as “It’s pouring heavily,” but BLEU only counts word matches, failing to recognize appropriate paraphrasing. - Inability to Grasp Context or Structure:
BLEU struggles with variations in word order. For instance, “The cat sat on the mat” and “On the mat sat the cat” carry the same meaning but score poorly due to different word arrangements. This limitation is especially problematic for nuanced legal translations where precision is paramount.
To overcome these shortcomings, other metrics such as ROUGE (measuring content recall), METEOR (considering synonyms and word order), and TER (counting edits needed to align with reference translations) are often used alongside BLEU.
✍🏻 The Relationship Between BLEU Scores and Translation Quality
A high BLEU score typically indicates that machine translations closely match human reference translations—both in word choice and structure. In highly specialized fields like legal translations, where accuracy and expertise are crucial, BLEU scores become an essential measure of reliability.
At Bering Lab, our BeringAI engine consistently achieves 2–6 times higher BLEU scores compared to popular tools like Google Translate, Papago, and DeepL—especially for patents, contracts, and case law. These superior scores demonstrate our engine’s precision and specialization in legal translations.
✍🏻 How BeringAI+ Delivers Unparalleled Quality
BeringAI+ combines the speed of AI translation with expert legal review to guarantee unmatched quality. Features include:
- Superior BLEU scores: Achieving accuracy for specialized legal terminology.
- Consistency across documents: Context-sensitive translations refined by human reviewers.
- Multilingual support: Simultaneous translation into over 30 languages while preserving formatting.
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Experience translation you can trust—where BLEU scores meet human expertise.
