Discriminative Latent Model (DLM) is proposed for Multiword Expressions (MWEs) extraction in Chinese text to improve the performance of Machine Translation (MT) system such as Template Based MT (TBMT).For MT systems to become of further practical use,they need to be enhanced with MWEs processing capability.As our study towards this goal,we propose DLM,which is developed for sequence labeling task including hidden structures,to extract MWEs for MT systems.DLM combines the advantages of existing discriminative models,which can learn hidden structures in sequence labeling task.In our evaluations,DLM achieves precisions ranging up to 90.73％ for some type of MWEs,which is higher than state-of-the-art discriminative models.Such results demonstrate that it is feasible to automatically identify many Chinese MWEs using our DLM tool.With MWEs processing model,BLEU score of MT-system has also been increased by up to 0.3 in close test.