giaabaoo/MTP_Dataset

Dataset Card for the MTP Dataset

Dataset Statistics

Statistic	Value
Total number of conversation videos	340
Total duration (h)	13.3
Total number of utterance-level videos	12,351
Total number of words in all transcripts	81,909
Average length of conversation transcripts	241.5
Maximum length of conversation transcripts	460
Average length of conversation videos (s)	1.9
Maximum length of conversation videos (m)	2.5
Total number of TPs videos	214

Examples

Please refer to this link for viewing the data samples.

Languages

English.

Dataset Creation

Please refer to the Annotation Guidelines section in our paper.

Additional Information

Licensing Information

The CC BY-NC-SA 4.0 license allows others to share and adapt a work as long as they give appropriate credit to the original creator, use the work for non-commercial purposes, and license any derivative works under the same terms. This promotes collaboration and ensures that adaptations remain accessible and open, while also protecting the creator's rights and intentions.

Citation Information

@article{bigbangtheory,
      title={The Big Bang Theory},
      author={Chuck Lorre and Bill Prady},
      year={2007},
      journal={CBS},
      url={https://www.cbs.com/shows/big_bang_theory/}
}

@inproceedings{ho-etal-2024-mtp,
    title = "{MTP}: A Dataset for Multi-Modal Turning Points in Casual Conversations",
    author = "Ho, Gia-Bao  and
      Tan, Chang  and
      Darban, Zahra  and
      Salehi, Mahsa  and
      Haf, Reza  and
      Buntine, Wray",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-short.30",
    pages = "314--326",
    abstract = "Detecting critical moments, such as emotional outbursts or changes in decisions during conversations, is crucial for understanding shifts in human behavior and their consequences. Our work introduces a novel problem setting focusing on these moments as turning points (TPs), accompanied by a meticulously curated, high-consensus, human-annotated multi-modal dataset. We provide precise timestamps, descriptions, and visual-textual evidence high-lighting changes in emotions, behaviors, perspectives, and decisions at these turning points. We also propose a framework, TPMaven, utilizing state-of-the-art vision-language models to construct a narrative from the videos and large language models to classify and detect turning points in our multi-modal dataset. Evaluation results show that TPMaven achieves an F1-score of 0.88 in classification and 0.61 in detection, with additional explanations aligning with human expectations.",
}

@article{ho2024mtp,
  title={MTP: A Dataset for Multi-Modal Turning Points in Casual Conversations},
  author={Ho, Gia-Bao Dinh and Tan, Chang Wei and Darban, Zahra Zamanzadeh and Salehi, Mahsa and Haffari, Gholamreza and Buntine, Wray},
  journal={arXiv preprint arXiv:2409.14801},
  url={arxiv.org/abs/2409.14801},
  year={2024}
}