Token-level and sequence-level loss smoothing for RNN language models
Table 4: Captioning performance on MSCOCO when training with sequence-level loss smoothing.
['[BOLD] Captioning with attention', '[BOLD] Captioning with attention Reward', '[BOLD] Captioning with attention V [ITALIC] sub', '[BOLD] Captioning with attention BLEU-1', '[BOLD] Captioning with attention BLEU-4', '[BOLD] Captioning with attention  \\textsc  [ITALIC] CIDEr']
We include results when computing BLEU-4 w.r.t. all reference sequences (like also done for CIDEr ), and when computing w.r.t. a single randomly selected reference sentence (as is the case for Hamming). With the BLEU-4 reward, using Vrefs yields best results in all but a single case. This underlines the effectiveness of sampling replacement words that are relevant to the task in sequence-level smoothing.