, ▁and ▁also ▁produce ▁good ▁correlation ▁with ▁human ▁jud g ement ▁at ▁the ▁sentence ▁or ▁segment ▁level . ▁This ▁diff ers ▁from ▁the ▁B LE U ▁metric ▁in ▁that ▁B LE U ▁see ks ▁correlation ▁at ▁the ▁cor pus ▁level . ▁ ▁Results ▁have ▁been ▁presented ▁which ▁give ▁correlation ▁of ▁up ▁to ▁ 0 . 9 6 4 ▁with ▁human ▁jud g ement ▁at ▁the ▁cor pus ▁level , ▁compared ▁to ▁B LE U ' s ▁achiev ement ▁of ▁ 0 . 8 1 7 ▁on ▁the ▁same ▁data ▁set . ▁At ▁the ▁sentence ▁level , ▁the ▁maximum ▁correlation ▁with ▁human ▁jud g ement ▁achieved ▁was ▁ 0 . 4 0 3 . ▁ ▁Algorithm ▁ ▁As ▁with ▁B LE U , ▁the ▁basic ▁unit ▁of ▁evaluation ▁is ▁the ▁sentence , ▁the ▁algorithm ▁first ▁creates ▁an ▁alignment ▁( see ▁illustr ations ) ▁between ▁two ▁sentences , ▁the ▁candidate ▁translation ▁string , ▁and ▁the ▁reference ▁translation ▁string . ▁The ▁alignment ▁is ▁a ▁set ▁of ▁ma ppings ▁between ▁un igr ams . ▁A ▁mapping ▁can ▁be ▁thought ▁of ▁as ▁a ▁line ▁between ▁a ▁un ig ram ▁in ▁one ▁string , ▁and ▁a ▁un ig ram ▁in ▁another ▁string . ▁The ▁constraints ▁are ▁as ▁follows ; ▁every ▁un ig ram ▁in ▁the ▁candidate ▁translation ▁must ▁map ▁to ▁zero ▁or ▁one ▁un ig ram ▁in ▁the ▁reference . ▁M app ings ▁are ▁selected ▁to ▁produce ▁an ▁alignment ▁as ▁defined ▁above . ▁If ▁there ▁are ▁two ▁align ments ▁with ▁the ▁same ▁number ▁of ▁ma ppings , ▁the ▁alignment