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.
@article{arxiv.1810.06195,
title = {Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism},
author = {Longyue Wang and Zhaopeng Tu and Andy Way and Qun Liu},
journal= {arXiv preprint arXiv:1810.06195},
year = {2018}
}