Fine-grained entity typing (FET) is an essential task in natural language
processing that aims to assign semantic types to entities in text. However, FET
poses a major challenge known as the noise labeling problem, whereby current
methods rely on estimating noise distribution to identify noisy labels but are
confused by diverse noise distribution deviation. To address this limitation,
we introduce Co-Prediction Prompt Tuning for noise correction in FET, which
leverages multiple prediction results to identify and correct noisy labels.
Specifically, we integrate prediction results to recall labeled labels and
utilize a differentiated margin to identify inaccurate labels. Moreover, we
design an optimization objective concerning divergent co-predictions during
fine-tuning, ensuring that the model captures sufficient information and
maintains robustness in noise identification. Experimental results on three
widely-used FET datasets demonstrate that our noise correction approach
significantly enhances the quality of various types of training samples,
including those annotated using distant supervision, ChatGPT, and
crowdsourcing.

为了解决细粒度实体类型任务中的噪声标注问题，引入了 Co-Prediction Prompt Tuning 方法，利用多个预测结果来识别和纠正错误标签，并设计了优化目标以考虑训练过程中的不一致预测，实验结果表明该方法显著提高了各种类型的训练样本的质量。