Question

When evaluating a machine translation system, why is the BLEU score calculation specifically designed to penalize overly short candidate translations using a brevity penalty?

Accepted Answer

The BLEU score evaluates machine translation by measuring precision, which calculates what percentage of the words in the candidate translation appear in a human-written reference translation. Because precision only considers how many words in the output are correct relative to the total number of words in the output, a system could achieve a perfect score by producing a single word that it knows for certain appears in the reference. For example, if the reference is &#x27;The cat is on the mat&#x27; and the machine outputs only the word &#x27;the&#x27;, the precision would be one hundred percent because &#x27;the&#x27; is indeed in the reference. However, this is a poor translation because it conveys almost no meaning. To prevent this, the brevity penalty is applied. The brevity penalty reduces the final BLEU score if the candidate translation is shorter than the reference translation. It forces the system to balance precision with recall by ensuring that the machine attempts to produce a translation of an appropriate length, thereby preventing the system from gaming the metric by outputting only highly confident but incomplete snippets of text.

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When evaluating a machine translation system, why is the BLEU score calculation specifically designed to penalize overly short candidate translations using a brevity penalty?