  Pronouns are frequently omitted in pro-drop languages, such as Chinese,
generally leading to significant challenges with respect to the production of
complete translations. To date, very little attention has been paid to the
dropped pronoun (DP) problem within neural machine translation (NMT). In this
work, we propose a novel reconstruction-based approach to alleviating DP
translation problems for NMT models. Firstly, DPs within all source sentences
are automatically annotated with parallel information extracted from the
bilingual training corpus. Next, the annotated source sentence is reconstructed
from hidden representations in the NMT model. With auxiliary training
objectives, in terms of reconstruction scores, the parameters associated with
the NMT model are guided to produce enhanced hidden representations that are
encouraged as much as possible to embed annotated DP information. Experimental
results on both Chinese-English and Japanese-English dialogue translation tasks
show that the proposed approach significantly and consistently improves
translation performance over a strong NMT baseline, which is directly built on
the training data annotated with DPs.
