Sequence labeling is a core task in text understanding for IE/IR systems. Text generation models have increasingly become the go-to solution for such tasks (e.g., entity extraction and dialog slot filling). While most research has focused on the labeling accuracy, a key aspect -- of vital practical importance -- has slipped through the cracks: understanding model confidence. More specifically, we lack a principled understanding of how to reliably gauge the confidence of a model in its predictions for each labeled span. This paper aims to provide some empirical insights on estimating model confidence for generative sequence labeling. Most notably, we find that simply using the decoder's output probabilities \textbf{is not} the best in realizing well-calibrated confidence estimates. As verified over six public datasets of different tasks, we show that our proposed approach -- which leverages statistics from top-$k$ predictions by a beam search -- significantly reduces calibration errors of the predictions of a generative sequence labeling model.

翻译：序列标注是信息检索/信息系统文本理解中的核心任务。文本生成模型已逐渐成为此类任务（如实体抽取和对话槽填充）的主流解决方案。尽管多数研究聚焦于标注准确性，但一个具有重要实践意义的关键方面却被忽视了：理解模型置信度。具体而言，我们缺乏对如何可靠衡量模型在每段标注跨度预测中置信度的原则性理解。本文旨在为生成式序列标注的置信度估计提供一些经验性见解。值得注意的是，我们发现仅使用解码器输出概率并不适合实现校准良好的置信度估计。通过在六个不同任务的公开数据集上验证，我们提出的方法——利用波束搜索中前k个预测的统计信息——能够显著降低生成式序列标注模型预测的校准误差。