No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling
Table 1: Automatic evaluation on the VIST dataset. We report BLEU (B), METEOR (M), ROUGH-L (R), and CIDEr (C) scores of the SOTA systems and the models we implemented, including XE-ss, GAN and AREL. AREL-s-N denotes AREL models with SoftSign as output activation and alternate frequency as N, while AREL-t-N denoting AREL models with Hyperbolic as the output activation (N = 50 or 100).
['Method', 'B-1', 'B-2', 'B-3', 'B-4', 'M', 'R', 'C']
We first implement a strong baseline model (XE-ss), which share the same architecture with our policy model but is trained with cross-entropy loss and scheduled sampling. Besides, we adopt the traditional generative adversarial training for comparison (GAN). We then use the XE-ss model to initialize our policy model and further train it with AREL. Evidently, our AREL model performs the best and achieves the new state-of-the-art results across all metrics.