aiqbench analysis
The paper proposes a training objective that combines focal loss with supervised contrastive learning to improve CEFR language-level prediction, which is a known challenge given imbalanced classes and fuzzy boundaries between levels. It reports gains over baselines at sentence and document levels, in both monolingual and multilingual settings, but the claimed state-of-the-art results still need independent replication on the named benchmarks. For readers, the practical takeaway is a loss-function change that might transfer to other imbalanced, ordinal text classification tasks.
CEFR Level Assessment across Languages and Text Granularities
arXiv:2610.41553v1 Announce Type: new Abstract: 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.
Abstract: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.
Computation and Language (cs.CL)
arXiv:2610.41553 [cs.CL]
(or arXiv:2610.41553v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.41553 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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