English(EN) LMSpell: Spell Correction with Pre-Trained Language Models

预训练语言模型在低资源语言拼写纠错方面展现潜力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员进行了一项实证研究，比较了三种预训练语言模型（PLMs）在拼写纠错方面的表现，特别关注低资源语言。研究发现，即使是像Gemma 3和mBART50这样较小的PLMs，在用5000句话的适度数据集进行微调后，也能超越传统的基于规则的拼写纠错器。对僧伽罗语的案例研究突显了资源有限语言拼写纠错的挑战和潜在解决方案。 AI

影响展示了利用现成的PLMs改进低资源语言拼写纠错的经济高效途径。

排序理由该集群包含一篇详细介绍新方法和实证研究的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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arXiv cs.CL TIER_1 English(EN) · Akesh Gunathilake, Nadil Karunarathna, Tharusha Bandaranayake, Nisansa de Silva, Surangika Ranathunga, Nevidu Jayatilleke · 2026-10-09 04:00

LMSpell：使用预训练语言模型进行拼写纠错

arXiv:2512.05414v4 Announce Type: replace Abstract: Spell correction is still a challenging problem for many languages, especially low-resource languages (LRLs). While pre-trained language models (PLMs) have been employed for spell correction, there has been no proper comparison …

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LMSpell：使用预训练语言模型进行拼写纠错

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