Unveiling the BLEU Score: Your Guide to Judging Machine Translation Quality

Author(s): VARUN MISHRA Originally published on Towards AI. Unveiling the BLEU Score: Your Guide to Judging Machine Translation Quality Machine translation has come a long way, from clunky rule-based systems to sleek neural models like Transformers. But how do we know if a machine’s translation is any good? Enter the BLEU score — a go-to metric for evaluating machine translation quality. Short for Bilingual Evaluation Understudy, BLEU is like a judge that compares a machine’s output to human translations. In this Medium blog, we’ll break down what BLEU is, dive into its math (don’t worry, it’s manageable!), and walk through a hands-on example with Python code. Whether you’re a data scientist, an NLP enthusiast, or just curious, this guide will make BLEU crystal clear. The article comprehensively explains the BLEU score as a critical metric for evaluating machine translation quality, covering its calculation method, strengths, and limitations. It details how BLEU calculates precision while considering brevity, providing insights into its practical applications and challenges, and concludes with advice on using BLEU alongside other evaluation metrics for a more rounded perspective on translation quality. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI