Datasets:
Languages:
English
Size:
n<1K
ArXiv:
Tags:
turning-point-detection
turning-point-classification
conversational-turning-point
conversational-dataset
License:
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}
}
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