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