---
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}
}
```