  Dropped Pronouns (DP) in which pronouns are frequently dropped in the source
language but should be retained in the target language are challenge in machine
translation. In response to this problem, we propose a semi-supervised approach
to recall possibly missing pronouns in the translation. Firstly, we build
training data for DP generation in which the DPs are automatically labelled
according to the alignment information from a parallel corpus. Secondly, we
build a deep learning-based DP generator for input sentences in decoding when
no corresponding references exist. More specifically, the generation is
two-phase: (1) DP position detection, which is modeled as a sequential
labelling task with recurrent neural networks; and (2) DP prediction, which
employs a multilayer perceptron with rich features. Finally, we integrate the
above outputs into our translation system to recall missing pronouns by both
extracting rules from the DP-labelled training data and translating the
DP-generated input sentences. Experimental results show that our approach
achieves a significant improvement of 1.58 BLEU points in translation
performance with 66% F-score for DP generation accuracy.

Computation and Language