
## Dataset Description

- **Repository:** [Link to repo](https://github.com/VityaVitalich/IMAD)
- **Paper:** [IMage Augmented multi-modal Dialogue: IMAD](https://arxiv.org/abs/2305.10512v1)
- **Point of Contact:** [Contacts Section](https://github.com/VityaVitalich/IMAD#contacts)

### Dataset Summary

This dataset contains data from the paper [IMage Augmented multi-modal Dialogue: IMAD](https://arxiv.org/abs/2305.10512v1).
The main feature of this dataset is the novelty of the task. It has been generated specifically for the purpose of image interpretation in a dialogue context.
Some of the dialogue utterances have been replaced with images, allowing a generative model to be trained to restore the initial utterance.
The dialogues are sourced from multiple dialogue datasets (DailyDialog, Commonsense, PersonaChat, MuTual, Empathetic Dialogues, Dream) and have been filtered using a technique described in the paper.
A significant portion of the data has been labeled by assessors, resulting in a high inter-reliability score. The combination of these methods has led to a well-filtered dataset and consequently a high BLEU score.
We hope that this dataset will be beneficial for the development of multi-modal deep learning.

### Data Fields

Dataset contains 5 fields

- `image_id`: `string` that contains id of image in the full Unsplash Dataset
- `source_data`: `string` that contains the name of source dataset
- `utter`: `string` that contains utterance that was replaced in this dialogue with an image
- `context`: `list` of `string` that contains sequence of utterances in the dialogue before the replaced utterance
- `image_like`: `int` that shows if the data was collected with assessors or via filtering technique


### Licensing Information

Textual part of IMAD is licensed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). Full Dataset with images could be requested directly contacting authors
or could be obtained with matching images_id with Unsplash full dataset.

### Contacts

Feel free to reach out to us at [vvmoskvoretskiy@yandex.ru] for inquiries, collaboration suggestions, or data requests related to our work.

### Citation Information


To cite this article please use this BibTex reference

```bibtex
@misc{viktor2023imad,
      title={IMAD: IMage-Augmented multi-modal Dialogue}, 
      author={Moskvoretskii Viktor and Frolov Anton and Kuznetsov Denis},
      year={2023},
      eprint={2305.10512},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

Or via MLA Citation

```
Viktor, Moskvoretskii et al. “IMAD: IMage-Augmented multi-modal Dialogue.” (2023).
```
task_categories:text-generation task_categories:image-to-text multilinguality:monolingual size_categories:1K<n<10K language:en license:cc-by-nc-4.0 multi-modal dialogue arxiv:2305.10512 region:us
{"language": ["en"], "license": "cc-by-nc-4.0", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "task_categories": ["text-generation", "image-to-text"], "pretty_name": "IMAD", "tags": ["multi-modal", "dialogue"]}
TAGS
#task_categories-text-generation #task_categories-image-to-text #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-cc-by-nc-4.0 #multi-modal #dialogue #arxiv-2305.10512 #region-us 


## Dataset Description

- Repository: Link to repo
- Paper: IMage Augmented multi-modal Dialogue: IMAD
- Point of Contact: Contacts Section

### Dataset Summary

This dataset contains data from the paper IMage Augmented multi-modal Dialogue: IMAD.
The main feature of this dataset is the novelty of the task. It has been generated specifically for the purpose of image interpretation in a dialogue context.
Some of the dialogue utterances have been replaced with images, allowing a generative model to be trained to restore the initial utterance.
The dialogues are sourced from multiple dialogue datasets (DailyDialog, Commonsense, PersonaChat, MuTual, Empathetic Dialogues, Dream) and have been filtered using a technique described in the paper.
A significant portion of the data has been labeled by assessors, resulting in a high inter-reliability score. The combination of these methods has led to a well-filtered dataset and consequently a high BLEU score.
We hope that this dataset will be beneficial for the development of multi-modal deep learning.

### Data Fields

Dataset contains 5 fields

- 'image_id': 'string' that contains id of image in the full Unsplash Dataset
- 'source_data': 'string' that contains the name of source dataset
- 'utter': 'string' that contains utterance that was replaced in this dialogue with an image
- 'context': 'list' of 'string' that contains sequence of utterances in the dialogue before the replaced utterance
- 'image_like': 'int' that shows if the data was collected with assessors or via filtering technique


### Licensing Information

Textual part of IMAD is licensed under CC BY-NC-SA 4.0. Full Dataset with images could be requested directly contacting authors
or could be obtained with matching images_id with Unsplash full dataset.

### Contacts

Feel free to reach out to us at [vvmoskvoretskiy@URL] for inquiries, collaboration suggestions, or data requests related to our work.




To cite this article please use this BibTex reference



Or via MLA Citation


## Dataset Description

- Repository: Link to repo
- Paper: IMage Augmented multi-modal Dialogue: IMAD
- Point of Contact: Contacts Section ### Dataset Summary

This dataset contains data from the paper IMage Augmented multi-modal Dialogue: IMAD.
The main feature of this dataset is the novelty of the task. It has been generated specifically for the purpose of image interpretation in a dialogue context.
Some of the dialogue utterances have been replaced with images, allowing a generative model to be trained to restore the initial utterance.
The dialogues are sourced from multiple dialogue datasets (DailyDialog, Commonsense, PersonaChat, MuTual, Empathetic Dialogues, Dream) and have been filtered using a technique described in the paper.
A significant portion of the data has been labeled by assessors, resulting in a high inter-reliability score. The combination of these methods has led to a well-filtered dataset and consequently a high BLEU score.
We hope that this dataset will be beneficial for the development of multi-modal deep learning. ### Data Fields

Dataset contains 5 fields

- 'image_id': 'string' that contains id of image in the full Unsplash Dataset
- 'source_data': 'string' that contains the name of source dataset
- 'utter': 'string' that contains utterance that was replaced in this dialogue with an image
- 'context': 'list' of 'string' that contains sequence of utterances in the dialogue before the replaced utterance
- 'image_like': 'int' that shows if the data was collected with assessors or via filtering technique ### Licensing Information

Textual part of IMAD is licensed under CC BY-NC-SA 4.0. Full Dataset with images could be requested directly contacting authors
or could be obtained with matching images_id with Unsplash full dataset. ### Contacts

Feel free to reach out to us at [vvmoskvoretskiy@URL] for inquiries, collaboration suggestions, or data requests related to our work.




To cite this article please use this BibTex reference



Or via MLA Citation
TAGS
#task_categories-text-generation #task_categories-image-to-text #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-cc-by-nc-4.0 #multi-modal #dialogue #arxiv-2305.10512 #region-us 
 ## Dataset Description

- Repository: Link to repo
- Paper: IMage Augmented multi-modal Dialogue: IMAD
- Point of Contact: Contacts Section ### Dataset Summary

This dataset contains data from the paper IMage Augmented multi-modal Dialogue: IMAD.
The main feature of this dataset is the novelty of the task. It has been generated specifically for the purpose of image interpretation in a dialogue context.
Some of the dialogue utterances have been replaced with images, allowing a generative model to be trained to restore the initial utterance.
The dialogues are sourced from multiple dialogue datasets (DailyDialog, Commonsense, PersonaChat, MuTual, Empathetic Dialogues, Dream) and have been filtered using a technique described in the paper.
A significant portion of the data has been labeled by assessors, resulting in a high inter-reliability score. The combination of these methods has led to a well-filtered dataset and consequently a high BLEU score.
We hope that this dataset will be beneficial for the development of multi-modal deep learning. ### Data Fields

Dataset contains 5 fields

- 'image_id': 'string' that contains id of image in the full Unsplash Dataset
- 'source_data': 'string' that contains the name of source dataset
- 'utter': 'string' that contains utterance that was replaced in this dialogue with an image
- 'context': 'list' of 'string' that contains sequence of utterances in the dialogue before the replaced utterance
- 'image_like': 'int' that shows if the data was collected with assessors or via filtering technique ### Licensing Information

Textual part of IMAD is licensed under CC BY-NC-SA 4.0. Full Dataset with images could be requested directly contacting authors
or could be obtained with matching images_id with Unsplash full dataset. ### Contacts

Feel free to reach out to us at [vvmoskvoretskiy@URL] for inquiries, collaboration suggestions, or data requests related to our work.




To cite this article please use this BibTex reference



Or via MLA Citation
passage: TAGS
#task_categories-text-generation #task_categories-image-to-text #multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-cc-by-nc-4.0 #multi-modal #dialogue #arxiv-2305.10512 #region-us 
## Dataset Description

- Repository: Link to repo
- Paper: IMage Augmented multi-modal Dialogue: IMAD
- Point of Contact: Contacts Section### Dataset Summary

This dataset contains data from the paper IMage Augmented multi-modal Dialogue: IMAD.
The main feature of this dataset is the novelty of the task. It has been generated specifically for the purpose of image interpretation in a dialogue context.
Some of the dialogue utterances have been replaced with images, allowing a generative model to be trained to restore the initial utterance.
The dialogues are sourced from multiple dialogue datasets (DailyDialog, Commonsense, PersonaChat, MuTual, Empathetic Dialogues, Dream) and have been filtered using a technique described in the paper.
A significant portion of the data has been labeled by assessors, resulting in a high inter-reliability score. The combination of these methods has led to a well-filtered dataset and consequently a high BLEU score.
We hope that this dataset will be beneficial for the development of multi-modal deep learning.### Data Fields

Dataset contains 5 fields

- 'image_id': 'string' that contains id of image in the full Unsplash Dataset
- 'source_data': 'string' that contains the name of source dataset
- 'utter': 'string' that contains utterance that was replaced in this dialogue with an image
- 'context': 'list' of 'string' that contains sequence of utterances in the dialogue before the replaced utterance
- 'image_like': 'int' that shows if the data was collected with assessors or via filtering technique