Story Ending Generation with Incremental Encoding and Commonsense Knowledge
Table 1: Automatic and manual evaluation results.
['Model', 'PPL', 'BLEU-1', 'BLEU-2', 'Gram.', 'Logic.']
Our models have lower perplexity and higher BLEU scores than the baselines. IE and IE+MSA have remarkably lower perplexity than other models. As for BLEU, IE+MSA(CA) obtained the highest BLEU-1 and BLEU-2 scores. This indicates that multi-source attention leads to generate story endings that have more overlaps with the reference endings.