Neural Machine Translation with Reconstruction
Table 5: Contributions of reconstruction from parameter training and reranking of candidates in testing.
['[BOLD] Rec. used in  [ITALIC] Training', '[BOLD] Rec. used in  [ITALIC] Testing', '[BOLD] Beam 10', '[BOLD] Beam 100']
The contribution of reconstruction is of two-fold: (1) enabling parameter training for generating better translation candidates, and (2) enabling better reranking of generated candidates in testing. When applied only in training, reconstruction improves translation performance by generating fluent and adequate translation candidates. On top of that, reconstruction-based reranking further improves the performance. The improvements are more significant when decoding spaces increase.