Translating Pro-Drop Languages with Reconstruction Models
Table 3: Translation performance improvement (“△”) with manually labelled DPs (“Oracle”).
['[BOLD] System', '[BOLD] Baseline', '[BOLD] Oracle', '△']
Ideally, the hidden states (either encoder-side or decoder-side) should embed the missing DP information by learning the alignments between bilingual pronouns from the training corpus. We make the following two observations: (1) the NMT model indeed outperform SMT model when translating pro-drop languages; and (2) the performance of the NMT model can be further improved by improving translation of DPs. In this work, we propose to improve DP translation by guiding hidden states to embed the missing DP information.