Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication
Table 1: Overall performance of story generation on VIST dataset for different models in terms of BLEU (B), METEOR (M), ROUGE-L (R-L), and CIDEr-D (C). Bolded numbers are the best performance in each column.
['Methods', 'B-1', 'B-2', 'B-3', 'B-4', 'R-L', 'C', 'M']
TAVST (MLE) outperforms all of the baseline models trained with MLE. This confirms the effectivness of topic information for generating better stories. Noticeably, compared with the RL-based models, our TAVST (MLE) has already achieved a competitive performance and outperforms other RL models (i.e., AREL and HSRL) in terms of METEOR and BLEU@[2-4] metrics. After equipped with RL, our TAVST (RL) model is able to further improve the performance, outperforming the two RL models in terms of all metrics except CIDEr-D. Our full model TAVST (both MLE and RL versions) outperforms TAVST w/o IU, which directly demonstrates the effectiveness of the IU module. TAVST w/o IU achieves better performance than VST, which proves that topic description generator can provide guidance for story generation.