Multilingual parliamentary model XLM-R-parla
This is the XLM-R-large model additionally pre-trained on texts of parliamentary proceedings. Texts for the additional pre-training, 1.7 billion words in size, come from the ParlaMint corpus and the EuroParl corpus.
The model is a result of the ParlaMint project. The details on the model development are described in the following paper:
@article{
Mochtak_Rupnik_Ljubešić_2023,
title={The ParlaSent multilingual training dataset for sentiment identification in parliamentary proceedings},
rights={All rights reserved},
url={http://arxiv.org/abs/2309.09783},
abstractNote={Sentiments inherently drive politics. How we receive and process information plays an essential role in political decision-making, shaping our judgment with strategic consequences both on the level of legislators and the masses. If sentiment plays such an important role in politics, how can we study and measure it systematically? The paper presents a new dataset of sentiment-annotated sentences, which are used in a series of experiments focused on training a robust sentiment classifier for parliamentary proceedings. The paper also introduces the first domain-specific LLM for political science applications additionally pre-trained on 1.72 billion domain-specific words from proceedings of 27 European parliaments. We present experiments demonstrating how the additional pre-training of LLM on parliamentary data can significantly improve the model downstream performance on the domain-specific tasks, in our case, sentiment detection in parliamentary proceedings. We further show that multilingual models perform very well on unseen languages and that additional data from other languages significantly improves the target parliament’s results. The paper makes an important contribution to multiple domains of social sciences and bridges them with computer science and computational linguistics. Lastly, it sets up a more robust approach to sentiment analysis of political texts in general, which allows scholars to study political sentiment from a comparative perspective using standardized tools and techniques.},
note={arXiv:2309.09783 [cs]},
number={arXiv:2309.09783},
publisher={arXiv},
author={Mochtak, Michal and Rupnik, Peter and Ljubešić, Nikola},
year={2023},
month={Sep},
language={en}
}
To cite the model itself, you can use
@misc{xlm-r-parla,
author = { Ljubešić, Nikola and Rupnik, Peter and van Noord, Rik },
title = { xlm-r-parla (Revision 8c7f9b2) },
year = 2023,
url = { https://huggingface.co/classla/xlm-r-parla },
doi = { 10.57967/hf/6717 },
publisher = { Hugging Face }
}
The first application of this model is the XLM-R-parlasent model, fine-tuned on the ParlaSent dataset for the task of sentiment analysis in parliamentary proceedings.