Using noisy crowdsourced labels from multiple annotators, a deep
learning-based end-to-end (E2E) system aims to learn the label correction
mechanism and the neural classifier simultaneously. To this end, many E2E
systems concatenate the neural classifier with multiple annotator-specific
``label confusion'' layers and co-train the two parts in a parameter-coupled
manner. The formulated coupled cross-entropy minimization (CCEM)-type criteria
are intuitive and work well in practice. Nonetheless, theoretical understanding
of the CCEM criterion has been limited. The contribution of this work is
twofold: First, performance guarantees of the CCEM criterion are presented. Our
analysis reveals for the first time that the CCEM can indeed correctly identify
the annotators' confusion characteristics and the desired ``ground-truth''
neural classifier under realistic conditions, e.g., when only incomplete
annotator labeling and finite samples are available. Second, based on the
insights learned from our analysis, two regularized variants of the CCEM are
proposed. The regularization terms provably enhance the identifiability of the
target model parameters in various more challenging cases. A series of
synthetic and real data experiments are presented to showcase the effectiveness
of our approach.

本文提出了一种基于深度学习的端到端系统，利用嘈杂的众包标签，通过多个注释器特定的 label confusion 层和参数耦合的方式，与神经分类器同时学习标签纠正机制，并提出了 CCEM 的正则化变种，以增强目标模型参数的可识别性。

众包标签深度学习：交叉熵最小化、可识别性和正则化

Deep Learning From Crowdsourced Labels: Coupled Cross-entropy  Minimization, Identifiability, and Regularization

Representing a true label as a one-hot vector is a common practice in
training text classification models. However, the one-hot representation may
not adequately reflect the relation between the instances and labels, as labels
are often not completely independent and instances may relate to multiple
labels in practice. The inadequate one-hot representations tend to train the
model to be over-confident, which may result in arbitrary prediction and model
overfitting, especially for confused datasets (datasets with very similar
labels) or noisy datasets (datasets with labeling errors). While training
models with label smoothing (LS) can ease this problem in some degree, it still
fails to capture the realistic relation among labels. In this paper, we propose
a novel Label Confusion Model (LCM) as an enhancement component to current
popular text classification models. LCM can learn label confusion to capture
semantic overlap among labels by calculating the similarity between instances
and labels during training and generate a better label distribution to replace
the original one-hot label vector, thus improving the final classification
performance. Extensive experiments on five text classification benchmark
datasets reveal the effectiveness of LCM for several widely used deep learning
classification models. Further experiments also verify that LCM is especially
helpful for confused or noisy datasets and superior to the label smoothing
method.

该研究提出了一种标签混淆模型作为当前流行的文本分类模型的增强组件，可以通过在训练期间计算实例和标签之间的相似性来捕获标签之间的语义重叠，从而生成更好的标签分布以取代原始的 one-hot 标签向量，以提高最终的分类性能。