Propicto/propicto-orfeo

---
license: cc-by-nc-sa-4.0
task_categories:
- translation
language:
- fr
tags:
- pictograms
- AAC
pretty_name: Propicto-orféo
---
# Propicto-orféo

## 📝 Dataset Description

Propicto-orféo is a dataset of aligned speech-id/transcription/pictograms (the pictograms correspond to the identifier associated with an ARASAAC pictogram) in French.
It was created from the CEFC-Orféo corpus. This dataset was presented in the research paper titled ["A Multimodal French Corpus of Aligned Speech, Text, and Pictogram Sequences for Speech-to-Pictogram Machine Translation](https://aclanthology.org/2024.lrec-main.76/)" at LREC-Coling 2024. The dataset was split into training, validation, and test sets.

Propicto-orféo contains three CSV files : train, valid and test, with the following statistics :
| **Split** | **Number of utterances** |
|:-----------:|:-----------------------:|
| train | 231 374 |
| valid | 28 796 |
| test | 29 009 |

## ⚒️ Dataset Structure

Each file contains the following information :
```csv
clips : the unique identifier of the utterance, which corresponds to a unique audio clip file (in wav) for the orféo dataset
text : the transcription of the audio clip
pictos : the sequence of id pictograms from ARASAAC
tokens : the sequence of tokens, each of them is the keyword associated to the ARASAAC id pictogram
```

## 💡 Dataset example

For the given sample :
```csv
clips : cefc-cfpb-1000-5-1186
text : tu essayes de mélanger les deux
pictos : [6625, 26144, 7074, 5515, 5367]
tokens : toi essayer de mélanger à_côté_de
```

- The `clips` is from the Orféo subcorpus [CFPB, 1000-5](https://orfeo.ortolang.fr/annis-sample/cfpb/CFPB-1000-5.html), with the sentence ID 1186.<br />
- The `text` is the associated transcription, in en : “you try to mix the two”.<br />
- `pictos` is the sequence of pictogram IDs, each of them can be retrieved from here : 6625 = https://static.arasaac.org/pictograms/6625/6625_2500.png<br />
- `tokens` are retrieved from a specific lexicon and can be used to train translation models.


## ℹ️ Dataset Sources

- **Repository:** [CEFC-Orféo](https://www.ortolang.fr/market/corpora/cefc-orfeo)
- **Papers :**
- [C. Benzitoun, J.-M. Debaisieux, H.-J. Deulofeu (2016). Le projet ORFÉO : un corpus d'études pour le français contemporain. Corpus n°15, p. 91-114](https://journals.openedition.org/corpus/2936)
- [J.-M. Debaisieux & C. Benzitoun (2020). Orféo : un corpus et une plateforme pour l’étude du français contemporain. Langages n°219](https://shs.cairn.info/revue-langages-2020-3?lang=fr)

## 💻 Uses

Propicto-orféo is intended to be used to train Speech-to-Pictograms translation and Text-to-Pictograms translation models.
This dataset can also be used to fine-tune large language models to perform translation into pictograms.

## ⚙️ Dataset Creation

The dataset is created by applying a specific formalism that converts french oral transcriptions into a corresponding sequence of pictograms.<br />
The formalism includes a set of grammatical rules to handle specific phenomenon (negation, name entities, pronominal form, plural, ...) to the French language, as well as a dictionary which associates each ARASAAC ID pictogram with a set of keywords (tokens).<br />
This formalism was presented at [LREC](https://aclanthology.org/2024.lrec-main.76/).

Source Data : conversations / meetings / daily life situations (oral transcriptions)

## ⁉️ Limitations

The translation can be partially incorrect, due to incorrect or missing words translated into pictograms.

## 💡 Information

- **Curated by:** Cécile MACAIRE
- **Funded by :** [PROPICTO ANR-20-CE93-0005](https://anr.fr/Projet-ANR-20-CE93-0005)
- **Language(s) (NLP):** French
- **License:** CC-BY-NC-SA-4.0

## 📌 Citation

```bibtex
@inproceedings{macaire-etal-2024-multimodal,
title = "A Multimodal {F}rench Corpus of Aligned Speech, Text, and Pictogram Sequences for Speech-to-Pictogram Machine Translation",
author = "Macaire, C{\'e}cile and
Dion, Chlo{\'e} and
Arrigo, Jordan and
Lemaire, Claire and
Esperan{\c{c}}a-Rodier, Emmanuelle and
Lecouteux, Benjamin and
Schwab, Didier",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
year = "2024",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.76",
pages = "839--849",
}

@inproceedings{macaire24_interspeech,
title = {Towards Speech-to-Pictograms Translation},
author = {Cécile Macaire and Chloé Dion and Didier Schwab and Benjamin Lecouteux and Emmanuelle Esperança-Rodier},
year = {2024},
booktitle = {Interspeech 2024},
pages = {857--861},
doi = {10.21437/Interspeech.2024-490},
issn = {2958-1796},
}
```

## 👩‍🏫 Dataset Card Authors

**Cécile MACAIRE, Chloé DION, Emmanuelle ESPÉRANÇA-RODIER, Benjamin LECOUTEUX, Didier SCHWAB**