# Enhanced Bilingual Evaluation Understudy

arXiv: https://arxiv.org/abs/1509.09088 · HTML: https://mlsanity.com/paper/1509.09088/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2015.

## Built on by (most cited first, 3 of 3)

- [Attention Mechanism and Context Modeling System for Text Mining Machine Translation](https://mlsanity.com/paper/2408.04216/) (2024)
- [Summarizing Radiology Reports Findings into Impressions](https://mlsanity.com/paper/2405.06802/) (2024)
- [Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks](https://mlsanity.com/paper/2604.15390/) (2026)

## Abstract (arXiv)

Our research extends the Bilingual Evaluation Understudy (BLEU) evaluation technique for statistical machine translation to make it more adjustable and robust. We intend to adapt it to resemble human evaluation more. We perform experiments to evaluate the performance of our technique against the primary existing evaluation methods. We describe and show the improvements it makes over existing methods as well as correlation to them. When human translators translate a text, they often use synonyms, different word orders or style, and other similar variations. We propose an SMT evaluation technique that enhances the BLEU metric to consider variations such as those.