Datasets:
Languages:
English
Size:
n<1K
ArXiv:
Tags:
turning-point-detection
turning-point-classification
conversational-turning-point
conversational-dataset
License:
| license: cc-by-nc-sa-4.0 | |
| language: | |
| - en | |
| tags: | |
| - turning-point-detection | |
| - turning-point-classification | |
| - conversational-turning-point | |
| - conversational-dataset | |
| size_categories: | |
| - n<1K | |
| viewer: false | |
| # Dataset Card for the MTP Dataset | |
| ## Table of Contents | |
| - [Table of Contents](#table-of-contents) | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Statistics](#dataset-statistics) | |
| - [Examples](#examples) | |
| - [Languages](#languages) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Additional Information](#additional-information) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| ## Dataset Description | |
| - 🏠 [Homepage](https://giaabaoo.github.io/TPD_website/) | |
| - 📁 [Repository](https://github.com/giaabaoo/MTP_pipeline/tree/main) | |
| - 📝 [Paper](https://aclanthology.org/2024.acl-short.30/) | |
| - 🪧 [Poster](https://drive.google.com/file/d/1K8GUORTLHO-s7PNJHtHQ0RWHo1GaCye7/view?usp=sharing) | |
| ### 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](https://drive.google.com/drive/folders/1Su1dbNCdCu6U28C92q7-0EoyoPnBNsbx?usp=sharing) 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} | |
| } | |
| ``` | |