In this work, we propose to improve the original model from two perspectives. First, we use a shared reconstructor to read hidden states from both encoder and decoder. Second, we integrate a DP predictor into NMT to jointly learn to translate and predict DPs. Incorporating these as two auxiliary loss terms can guide both the encoder and decoder states to learn critical information relevant to DPs. Experimental results on a largescale Chinese-English subtitle corpus show that the two modifications can accumulatively improve translation performance, and the best result is +1.5 BLEU points better than that reported by <cite>Wang et al. (2018)</cite> .