Abstract
Sequence-to-sequence models have shown promising improvements on the temporal task of video captioning, but they optimize word-level cross-entropy loss during training. First, using policy gradient and mixed-loss methods for reinforcement learning, we directly optimize sentence-level task-based metrics (as rewards), achieving significant improvements over the baseline, based on both automatic metrics and human evaluation on multiple datasets. Next, we propose a novel entailment-enhanced reward (CIDEnt) that corrects phrase-matching based metrics (such as CIDEr) to only allow for logically-implied partial matches and avoid contradictions, achieving further significant improvements over the CIDEr-reward model. Overall, our CIDEnt-reward model achieves the new state-of-the-art on the MSR-VTT dataset.
Abstract (translated)
序列 - 序列模型已经显示出对视频字幕的时间任务的有希望的改进，但是它们优化了训练期间的字级交叉熵损失。首先，使用策略梯度和混合损失方法进行强化学习，我们基于多个数据集上的自动度量和人为评估，直接优化基于任务的句子级度量（作为奖励），实现了基线的显着改善。接下来，我们提出了一种新颖的蕴涵增强奖励（CIDEnt），用于纠正基于短语匹配的指标（如CIDEr），以仅允许逻辑暗示的部分匹配并避免矛盾，对CIDEr奖励模型进行进一步的重大改进。总体而言，我们的CIDEnt-reward模型实现了MSR-VTT数据集中的最新技术。
URL
https://arxiv.org/abs/1708.02300
