Leveraging Sentence Similarity in Natural Language Generation:Improving Beam Search using Range Voting
Table 1: BLEU-1 and BLEU-4 scores obtained on the MSCOCO validation images.
['Beam size  [ITALIC] k', 'BLEU-1 1', 'BLEU-1 2', 'BLEU-1 10', 'BLEU-1 100', 'BLEU-4 1', 'BLEU-4 2', 'BLEU-4 10', 'BLEU-4 100']
4.1.1 BLEU scores Bird et al., The bigram similarity measures and the lstm_states measure improve BLEU scores for almost all beam sizes. In contrast, diverse decoding has almost no effect on BLEU, while length normalisation performs worse than standard beam search. The best result with our similarity metrics is achieved by lstm_states at k=100. Using smoothed_bleu4 and increasing the beam size to k=100 gives the overall best results.