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Update documentation card of miam dataset #4846

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Aug 14, 2022
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Update documentation card of miam dataset #4846

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Update README.md
PierreColombo Aug 13, 2022
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Fix dataset card
albertvillanova Aug 14, 2022
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31 changes: 21 additions & 10 deletions datasets/miam/README.md
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Expand Up @@ -240,9 +240,9 @@ For the `vm2` configuration, the different fields are:

## Additional Information

### Benchmark Curators
### Dataset Curators

Anonymous
Anonymous.

### Licensing Information

Expand All @@ -251,13 +251,24 @@ This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareA
### Citation Information

```
@unpublished{
anonymous2021cross-lingual,
title={Cross-Lingual Pretraining Methods for Spoken Dialog},
author={Anonymous},
journal={OpenReview Preprint},
year={2021},
url{https://openreview.net/forum?id=c1oDhu_hagR},
note={anonymous preprint under review}
@inproceedings{colombo-etal-2021-code,
title = "Code-switched inspired losses for spoken dialog representations",
author = "Colombo, Pierre and
Chapuis, Emile and
Labeau, Matthieu and
Clavel, Chlo{\'e}",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.656",
doi = "10.18653/v1/2021.emnlp-main.656",
pages = "8320--8337",
abstract = "Spoken dialogue systems need to be able to handle both multiple languages and multilinguality inside a conversation (\textit{e.g} in case of code-switching). In this work, we introduce new pretraining losses tailored to learn generic multilingual spoken dialogue representations. The goal of these losses is to expose the model to code-switched language. In order to scale up training, we automatically build a pretraining corpus composed of multilingual conversations in five different languages (French, Italian, English, German and Spanish) from OpenSubtitles, a huge multilingual corpus composed of 24.3G tokens. We test the generic representations on MIAM, a new benchmark composed of five dialogue act corpora on the same aforementioned languages as well as on two novel multilingual tasks (\textit{i.e} multilingual mask utterance retrieval and multilingual inconsistency identification). Our experiments show that our new losses achieve a better performance in both monolingual and multilingual settings.",
}
```

### Contributions

Thanks to [@eusip](https://github.com/eusip) and [@PierreColombo](https://github.com/PierreColombo) for adding this dataset.