论文标题
上下文感知独立的神经拼写校正
Context-aware Stand-alone Neural Spelling Correction
论文作者
论文摘要
现有的自然语言处理系统容易受到拼写错误导致的嘈杂输入的影响。相反，人类可以轻松地从拼写错误和周围环境中推断出相应的正确单词。受此启发的启发，我们解决了独立的拼写校正问题，该问题仅通过使用拼写信息和全局上下文表示，仅纠正每个令牌而没有其他令牌插入或删除的拼写。我们提出了一个简单而强大的解决方案，该解决方案可以通过微调预训练的语言模型来共同检测并纠正拼写错误作为序列标记任务。我们的解决方案的表现优于先前的最先进结果，绝对F0.5得分为12.8％。
Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings. On the contrary, humans can easily infer the corresponding correct words from their misspellings and surrounding context. Inspired by this, we address the stand-alone spelling correction problem, which only corrects the spelling of each token without additional token insertion or deletion, by utilizing both spelling information and global context representations. We present a simple yet powerful solution that jointly detects and corrects misspellings as a sequence labeling task by fine-turning a pre-trained language model. Our solution outperforms the previous state-of-the-art result by 12.8% absolute F0.5 score.
