This paper presents a simple but effective sentence-length informed method to select informative sentences for active learning (AL) based SMT. A length factor is introduced to penalize short sentences to balance the “
” and “
” problem. The penalty is dynamically updated at each iteration of sentence selection by the ratio of the current candidate sentence length and the overall average sentence length of the monolingual corpus. Experimental results on NIST Chinese–English pair and WMT French-English pair show that the proposed sentence-length penalty based method performs best compared with the typical selection method and random selection strategy.