  Dropped pronouns (DPs) are ubiquitous in pro-drop languages like Chinese,
Japanese etc. Previous work mainly focused on painstakingly exploring the
empirical features for DPs recovery. In this paper, we propose a neural
recovery machine (NRM) to model and recover DPs in Chinese, so that to avoid
the non-trivial feature engineering process. The experimental results show that
the proposed NRM significantly outperforms the state-of-the-art approaches on
both two heterogeneous datasets. Further experiment results of Chinese zero
pronoun (ZP) resolution show that the performance of ZP resolution can also be
improved by recovering the ZPs to DPs.
