WriterForcing: Generating more interesting story endings
Table 3: Model comparison based on automatic metrics DIST-1, DIST-2 and DIST-3.
['[BOLD] Model', '[BOLD] DIST-1', '[BOLD] DIST-2', '[BOLD] DIST-3']
, we observe that the Seq2Seq model and the incremental encoding + graph attention (IE + GA) model have the worst performance in diversity. Although it has been shown that the IE + GA model achieves a good BLEU score, we observe that the model does not do as well on our automated metrics like DIST-1, 2 and 3 because the model has learnt to generate endings which match the distribution as a whole instead of generating story specific endings.