Non-Monotonic Sequential Text Generation
Table 7: LSTM Policy results for machine translation experiments.
['[BOLD] Oracle', '[BOLD] Validation  [BOLD] Bleu', '[BOLD] Validation (BP)', '[BOLD] Validation  [BOLD] Meteor', '[BOLD] Validation  [BOLD] YiSi', '[BOLD] Validation  [BOLD] Ribes', '[BOLD] Test  [BOLD] Bleu', '[BOLD] Test (BP)', '[BOLD] Test  [BOLD] Meteor', '[BOLD] Test  [BOLD] YiSi', '[BOLD] Test  [BOLD] Ribes']
First focusing on the non-monotonic models, we see that the annealed policy outperforms the uniform policy on all metrics, with tree-encoding yielding further gains. Adding ⟨end⟩ tuning to the tree-encoding model decreases the Ribes score but improves the other metrics, notably increasing the Bleu brevity penalty.