Text classifiers built on Pre-trained Language Models (PLMs) have achieved
remarkable progress in various tasks including sentiment analysis, natural
language inference, and question-answering. However, the occurrence of
uncertain predictions by these classifiers poses a challenge to their
reliability when deployed in practical applications. Much effort has been
devoted to designing various probes in order to understand what PLMs capture.
But few studies have delved into factors influencing PLM-based classifiers'
predictive uncertainty. In this paper, we propose a novel framework, called
CUE, which aims to interpret uncertainties inherent in the predictions of
PLM-based models. In particular, we first map PLM-encoded representations to a
latent space via a variational auto-encoder. We then generate text
representations by perturbing the latent space which causes fluctuation in
predictive uncertainty. By comparing the difference in predictive uncertainty
between the perturbed and the original text representations, we are able to
identify the latent dimensions responsible for uncertainty and subsequently
trace back to the input features that contribute to such uncertainty. Our
extensive experiments on four benchmark datasets encompassing linguistic
acceptability classification, emotion classification, and natural language
inference show the feasibility of our proposed framework. Our source code is
available at: this https URL

该论文提出了一种名为 CUE 的新框架，通过一个变分自动编码器将预训练语言模型编码表示映射到一个潜在空间并通过扰动潜在空间生成文本表示，从而比较扰动前后的预测不确定性差异以识别引起不确定性的潜在维度并进一步追溯贡献于不确定性的输入特征。该框架在包括语言可接受性分类、情感分类和自然语言推理在内的四个基准数据集上进行了广泛的实验，可行性得到证明。

基于预训练语言模型的文本分类器不确定性解释框架

CUE: An Uncertainty Interpretation Framework for Text Classifiers Built  on Pre-Trained Language Models

Quotation extraction aims to extract quotations from written text. There are
three components in a quotation: source refers to the holder of the quotation,
cue is the trigger word(s), and content is the main body. Existing solutions
for quotation extraction mainly utilize rule-based approaches and sequence
labeling models. While rule-based approaches often lead to low recalls,
sequence labeling models cannot well handle quotations with complicated
structures. In this paper, we propose the Context and Former-Label Enhanced Net
(CofeNet) for quotation extraction. CofeNet is able to extract complicated
quotations with components of variable lengths and complicated structures. On
two public datasets (i.e., PolNeAR and Riqua) and one proprietary dataset
(i.e., PoliticsZH), we show that our CofeNet achieves state-of-the-art
performance on complicated quotation extraction.

本文提出了上下文和先前标签增强网络 (CofeNet)，针对文本中具有不同长度和复杂结构的引语进行提取，相比于基于规则和序列标注模型的方法，CofeNet 能够在 PolNeAR、Riqua 和 PoliticsZH 数据集上取得最先进的性能。