VSTAR: A Video-grounded Dialogue Dataset for Situated Semantic Understanding with Scene and Topic Transitions

Yuxuan Wang, Zilong Zheng, Xueliang Zhao, Jinpeng Li 0003, Yueqian Wang, Dongyan Zhao 0001. VSTAR: A Video-grounded Dialogue Dataset for Situated Semantic Understanding with Scene and Topic Transitions. In Anna Rogers, Jordan L. Boyd-Graber, Naoaki Okazaki, editors, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023. pages 5036-5048, Association for Computational Linguistics, 2023. [doi]

@inproceedings{WangZZ0WZ23,
  title = {VSTAR: A Video-grounded Dialogue Dataset for Situated Semantic Understanding with Scene and Topic Transitions},
  author = {Yuxuan Wang and Zilong Zheng and Xueliang Zhao and Jinpeng Li 0003 and Yueqian Wang and Dongyan Zhao 0001},
  year = {2023},
  url = {https://aclanthology.org/2023.acl-long.276},
  researchr = {https://researchr.org/publication/WangZZ0WZ23},
  cites = {0},
  citedby = {0},
  pages = {5036-5048},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023},
  editor = {Anna Rogers and Jordan L. Boyd-Graber and Naoaki Okazaki},
  publisher = {Association for Computational Linguistics},
  isbn = {978-1-959429-72-2},
}