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arXiv · 1810.06195

Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism

Longyue Wang·Zhaopeng Tu·Andy Way·Qun Liu
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Abstract

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.

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Longyue Wang, Zhaopeng Tu, Andy Way, Qun Liu. 2018-10-15. Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism. https://arxiv.org/abs/1810.06195

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