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CEFR Level Assessment across Languages and Text Granularities — arXiv:2610.41553

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41553
License (as archived): CC0-1.0
Archived: 2026-11-02T07:03:48.498Z
SHA-256 of the archived text: 4984a2d8d5aa858d18af3b9c24837f35b33a3d759936802c1eb05d9691d56f03
Stable URL of this page: https://metraipolis.com/documento/dc_d1fe2a67c5dc

How to cite

CEFR Level Assessment across Languages and Text Granularities — arXiv:2610.41553 — arXiv (en), https://arxiv.org/abs/2610.41553 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d1fe2a67c5dc (Metraipolis archive dc_d1fe2a67c5dc, sha256 4984a2d8d5aa858d18af3b9c24837f35b33a3d759936802c1eb05d9691d56f03)

BibTeX

@misc{dcd1fe2a67c5dc,
  title = {CEFR Level Assessment across Languages and Text Granularities — arXiv:2610.41553},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41553},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d1fe2a67c5dc; sha256 4984a2d8d5aa858d18af3b9c24837f35b33a3d759936802c1eb05d9691d56f03},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41553. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Automatic assessment of textual CEFR levels is an important task in educational natural language processing. However, accurate CEFR level prediction remains challenging due to the naturally imbalanced distribution of CEFR levels and the ambiguous boundaries between adjacent levels. To address these challenges, we propose a unified training objective that combines focal loss with supervised contrastive learning. Focal loss mitigates class imbalance by emphasizing underrepresented and difficult examples, while supervised contrastive learning learns more discriminative representations by encouraging intra-class compactness and inter-class separation. We evaluate the proposed method on sentence- and document-level CEFR assessment in both monolingual and multilingual settings. Experimental results demonstrate that our method consistently outperforms strong baselines across all settings, establishing new state-of-the-art performance on multiple CEFR assessment benchmarks.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.41553 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-02T06:57:13.222Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).