将拼音缩写准确转为汉字，提升中文纠错能力。
CNMBERT: A Model for Converting Hanyu Pinyin Abbreviations to Chinese Characters
- 把拼音缩写转汉字当作填空任务，用多掩码和专家混合模型解决信息不足问题。
- 在10373条数据上达到61.53%的排序准确率和51.86%的精确度。
- 适合需要精准中文输入的场景，如命名实体识别与情感分析。
将汉语拼音缩写转换为汉字是中文拼写纠正（CSC）领域的重要分支，在命名实体识别、情感分析等下游任务中具有关键作用。该任务通常涉及文本长度对齐，看似简单，但因拼音缩写信息量有限，准确转换极具挑战。本文将其视为填空任务，提出CNMBERT（zh-CN拼音多掩码BERT模型），通过引入多掩码策略与专家混合（MoE）层，显著提升性能。在包含10,373个样本的测试集上，该模型相较微调后的GPT模型及ChatGPT-4o，实现了61.53%的平均秩次召回率（MRR）与51.86%的准确率。
原文摘要 · Abstract (English)
The task of converting Hanyu Pinyin abbreviations to Chinese characters is a significant branch within the domain of Chinese Spelling Correction (CSC). It plays an important role in many downstream applications such as named entity recognition and sentiment analysis. This task typically involves text-length alignment and seems easy to solve; however, due to the limited information content in pinyin abbreviations, achieving accurate conversion is challenging. In this paper, we treat this as a fill-mask task and propose CNMBERT, which stands for zh-CN Pinyin Multi-mask BERT Model, as a solution to this issue. By introducing a multi-mask strategy and Mixture of Experts (MoE) layers, CNMBERT outperforms fine-tuned GPT models and ChatGPT-4o with a 61.53% MRR score and 51.86% accuracy on a 10,373-sample test dataset.
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