1.The motivation to investigate a graph structured model is to capture the global dependency structure in the sentence which different from existing sequence models that tend to focus on the dependency between each word and its close preceding words. However,the encoder and decoder is based on Transformer，which can draw global dependencies in sentence. Therefore, I am a bit confused about the complex approach of this article.
2.Table3 does not use the C3D feature to do the experiment, I think this paper should compare the result of C3D features in MSR-VTT with [1].On the MSR-VTT dataset, the performance of the method has not improved much on CIDEr, within 0.3%.
3.In the ablation experiment, the performance without reinforcement learning dropped lower than without dependency tree.The two tables do not list the cases where dependency tree and RL are not used.
4.Constructing the generation tree, constructing the ground-truth dependency trees, calculating loss and reward all consume many computing resources. Is the time cost of the training process large?
[1] Zhang at al.Object Relational Graph with Teacher-Recommended Learning for Video Captioning.CVPR'2020 Typos:e.g.The last two rows of table3 are wrongly blackened on the METEOR on dataset MSR-VTT.
3.In the ablation experiment, the performance without reinforcement learning dropped lower than without dependency tree.The two tables do not list the cases where dependency tree and RL are not used.