Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism
Longyue Wang, Zhaopeng Tu, Andy Way, Qun Liu · 2018
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.