In this paper, we address the limitations of the common data annotation and
training methods for objective single-label classification tasks. Typically,
when annotating such tasks annotators are only asked to provide a single label
for each sample and annotator disagreement is discarded when a final hard label
is decided through majority voting. We challenge this traditional approach,
acknowledging that determining the appropriate label can be difficult due to
the ambiguity and lack of context in the data samples. Rather than discarding
the information from such ambiguous annotations, our soft label method makes
use of them for training. Our findings indicate that additional annotator
information, such as confidence, secondary label and disagreement, can be used
to effectively generate soft labels. Training classifiers with these soft
labels then leads to improved performance and calibration on the hard label
test set.

本文主要研究了数据注释和训练方法在客观单标签分类任务中的局限性，并提出了使用软标签的方法来改善性能。

不浪费任何一个注释：通过软标签改进单标签分类器

Don't Waste a Single Annotation: Improving Single-Label Classifiers  Through Soft Labels

Computational complexity and overthinking problems have become the
bottlenecks for pre-training language models (PLMs) with millions or even
trillions of parameters. A Flexible-Patience-Based Early Exiting method
(F-PABEE) has been proposed to alleviate the problems mentioned above for
single-label classification (SLC) and multi-label classification (MLC) tasks.
F-PABEE makes predictions at the classifier and will exit early if predicted
distributions of cross-layer are consecutively similar. It is more flexible
than the previous state-of-the-art (SOTA) early exiting method PABEE because it
can simultaneously adjust the similarity score thresholds and the patience
parameters. Extensive experiments show that: (1) F-PABEE makes a better
speedup-accuracy balance than existing early exiting strategies on both SLC and
MLC tasks. (2) F-PABEE achieves faster inference and better performances on
different PLMs such as BERT and ALBERT. (3) F-PABEE-JSKD performs best for
F-PABEE with different similarity measures.

F-PABEE 方法通过在分类器进行预测，如果跨层预测分布连续相似，则提前退出，相对于之前最先进的 PABEE 早期退出方法更灵活，同时可以调整相似度分数阈值和耐心参数，通过实验证明，在单标签分类和多标签分类任务中，在 BERT 和 ALBERT 等不同 PLMs 上实现更快速的推理和更好的性能。

F-PABEE: 用于单标签和多标签文本分类任务的灵活耐心型早期退出

F-PABEE: Flexible-patience-based Early Exiting for Single-label and  Multi-label text Classification Tasks

Recently, end-to-end neural diarization (EEND) is introduced and achieves
promising results in speaker-overlapped scenarios. In EEND, speaker diarization
is formulated as a multi-label prediction problem, where speaker activities are
estimated independently and their dependency are not well considered. To
overcome these disadvantages, we employ the power set encoding to reformulate
speaker diarization as a single-label classification problem and propose the
overlap-aware EEND (EEND-OLA) model, in which speaker overlaps and dependency
can be modeled explicitly. Inspired by the success of two-stage hybrid systems,
we further propose a novel Two-stage OverLap-aware Diarization framework (TOLD)
by involving a speaker overlap-aware post-processing (SOAP) model to
iteratively refine the diarization results of EEND-OLA. Experimental results
show that, compared with the original EEND, the proposed EEND-OLA achieves a
14.39% relative improvement in terms of diarization error rates (DER), and
utilizing SOAP provides another 19.33% relative improvement. As a result, our
method TOLD achieves a DER of 10.14% on the CALLHOME dataset, which is a new
state-of-the-art result on this benchmark to the best of our knowledge.

本文旨在通过两种新的端到端神经会话分离 (EEND) 模型来处理讲话者活动的重合和联系，分别为基于幂集编码的重合感知 EEND-OLA 和基于两阶段混合系统的 TOLD，结果表明相较于原始 EEND，EEND-OLA 在音频行话数据集上 DER 的表现提升了 14.39％，而使用 SOAP 则提供了另外 19.33％的相对提升，最终 TOLD 在该数据集上取得了新的最佳结果 (10.14％DER)。

TOLD：一种新颖的两阶段重叠感知演讲人分离框架

TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker Diarization

In the context of single-label classification, despite the huge success of
deep learning, the commonly used cross-entropy loss function ignores the
intricate inter-class relationships that often exist in real-life tasks such as
age classification. In this work, we propose to leverage these relationships
between classes by training deep nets with the exact squared Earth Mover's
Distance (also known as Wasserstein distance) for single-label classification.
The squared EMD loss uses the predicted probabilities of all classes and
penalizes the miss-predictions according to a ground distance matrix that
quantifies the dissimilarities between classes. We demonstrate that on datasets
with strong inter-class relationships such as an ordering between classes, our
exact squared EMD losses yield new state-of-the-art results. Furthermore, we
propose a method to automatically learn this matrix using the CNN's own
features during training. We show that our method can learn a ground distance
matrix efficiently with no inter-class relationship priors and yield the same
performance gain. Finally, we show that our method can be generalized to
applications that lack strong inter-class relationships and still maintain
state-of-the-art performance. Therefore, with limited computational overhead,
one can always deploy the proposed loss function on any dataset over the
conventional cross-entropy.

本研究提出将 Wasserstein 距离应用于单标签分类，以考虑现实任务中存在的类别间联系，并表明该方法在具有明显类间关系的数据集上表现出最新的最优结果。