Traitement quantique des langues : \'etat de l'art
<|reference_start|>Traitement quantique des langues : \'etat de l'art: This article presents a review of quantum computing research works for Natural Language Processing (NLP). Their goal is to improve the performance of current models, and to provide a better representation of several linguistic phenomena, such as ambiguity and long range dependencies. Several families of approaches are presented, including symbolic diagrammatic approaches, and hybrid neural networks. These works show that experimental studies are already feasible, and open research perspectives on the conception of new models and their evaluation.<|reference_end|>
@article{campano2024traitement,
    title={Traitement quantique des langues : {\'e}tat de l'art},
    author={Sabrina Campano, Tahar Nabil, Meryl Bothua},
    journal={Revue TAL : traitement automatique des langues, 2023, 64 (1)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15370},
    primaryClass={cs.CL}
}