Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism
By Longyue Wang, Zhaopeng Tu, Andy Way, Qun Liu. 2018
27 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Natural Language Processing
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Citations in the corpus
- Scalable Cross Lingual Pivots to Model Pronoun Gender for Translation (2020, background): “Specific to the translation of implicit or dropped pronouns, Wang et al. (2018) shows that modeling pronoun prediction jointly with translation improves quality, while Miculicich Werlen and Popescu-Belis (2017a) presents a proof-of-concept where coreference links are available at decoding time.”