@InProceedings{pasunuru-bansal:2017:EMNLP2017,
  author    = {Pasunuru, Ramakanth  and  Bansal, Mohit},
  title     = {Reinforced Video Captioning with Entailment Rewards},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {979--985},
  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.},
  url       = {https://www.aclweb.org/anthology/D17-1103}
}