  Pronouns are frequently omitted in pro-drop languages, such as Chinese,
generally leading to significant challenges with respect to the production of
complete translations. Recently, Wang et al. (2018) proposed a novel
reconstruction-based approach to alleviating dropped pronoun (DP) translation
problems for neural machine translation models. In this work, we improve the
original model from two perspectives. First, we employ a shared reconstructor
to better exploit encoder and decoder representations. Second, we jointly learn
to translate and predict DPs in an end-to-end manner, to avoid the errors
propagated from an external DP prediction model. Experimental results show that
our approach significantly improves both translation performance and DP
prediction accuracy.
