@inproceedings{le-etal-2026-pantagruel,
    title = "Pantagruel: Unified Self-Supervised Encoders for {F}rench Text and Speech",
    author = "Le, Phuong-Hang  and
      Pelloin, Valentin  and
      Chatelain, Arnault  and
      Bouziane, Maryem  and
      Ghennai, Mohammed  and
      Guan, Qianwen  and
      Milintsevich, Kirill  and
      Mdhaffar, Salima  and
      Mannion, Aidan  and
      Defauw, Nils  and
      Gu, Shuyue  and
      Audibert, Alexandre Daniel  and
      Dinarelli, Marco  and
      Est{\`e}ve, Yannick  and
      Goeuriot, Lorraine  and
      Lalande, Steffen  and
      Herv{\'e}, Nicolas  and
      Coavoux, Maximin  and
      Portet, Fran{\c{c}}ois  and
      Ollion, {\'E}tienne  and
      Candito, Marie  and
      Peyrard, Maxime  and
      Rossato, Solange  and
      Lecouteux, Benjamin  and
      Nardy, Aur{\'e}lie  and
      S{\'e}rasset, Gilles  and
      Segonne, Vincent  and
      Evain, Sol{\`e}ne  and
      Fabre, Diandra  and
      Schwab, Didier",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://aclanthology.org/2026.lrec-1.799/",
    doi = "10.63317/573q4exhmpgd",
    pages = "10168--10191",
    abstract = "We release Pantagruel models, a new family of self-supervised encoder models for French text and speech. Instead of predicting modality-tailored targets such as textual tokens or speech units, Pantagruel learns contextualized target representations in the feature space, allowing modality-specific encoders to capture linguistic and acoustic regularities more effectively. Separate models are pre-trained on large-scale French corpora, including Wikipedia, OSCAR and CroissantLLM for text, together with MultilingualLibriSpeech, LeBenchmark, and INA-100k for speech. INA-100k is a newly introduced 100,000-hour corpus of French audio derived from the archives of the Institut National de l{'}Audiovisuel (INA), the national repository of French radio and television broadcasts, providing highly diverse audio data. We evaluate Pantagruel across a broad range of downstream tasks spanning both modalities, including those from the standard French benchmarks such as FLUE or LeBenchmark. Across these tasks, Pantagruel models show competitive or superior performance compared to strong French baselines such as CamemBERT, FlauBERT, and LeBenchmark2.0, while maintaining a shared architecture that can seamlessly handle either speech or text inputs. These results confirm the effectiveness of feature-space self-supervised objectives for French representation learning and highlight Pantagruel as a robust foundation for multimodal speech-text understanding."
}