Token-level and sequence-level loss smoothing for RNN language models
Table 1: MS-COCO ’s test set evaluation measures.
['[BOLD] With attention', '[BOLD] With attention BLEU-1', '[BOLD] With attention BLEU-4', '[BOLD] With attention CIDEr']
In fact, using these subsets introduces a useful bias to the model and improves performance. This improvement is most notable using the CIDEr reward that scores candidate sequences w.r.t. to multiple references, which stabilizes the scoring of the candidates.