Partial-label learning is a popular weakly supervised learning setting that
allows each training example to be annotated with a set of candidate labels.
Previous studies on partial-label learning only focused on the classification
setting where candidate labels are all discrete, which cannot handle continuous
labels with real values. In this paper, we provide the first attempt to
investigate partial-label regression, where each training example is annotated
with a set of real-valued candidate labels. To solve this problem, we first
propose a simple baseline method that takes the average loss incurred by
candidate labels as the predictive loss. The drawback of this method lies in
that the loss incurred by the true label may be overwhelmed by other false
labels. To overcome this drawback, we propose an identification method that
takes the least loss incurred by candidate labels as the predictive loss. We
further improve it by proposing a progressive identification method to
differentiate candidate labels using progressively updated weights for incurred
losses. We prove that the latter two methods are model-consistent and provide
convergence analyses. Our proposed methods are theoretically grounded and can
be compatible with any models, optimizers, and losses. Experiments validate the
effectiveness of our proposed methods.

本文提出了首个对部分标签回归进行研究的简单基线和改进型候选标签鉴定方法，并证明了这两种方法的模型一致性和收敛分析，在实验中验证了其有效性。

部分标签回归

Partial-Label Regression

Abbreviations present a significant challenge for NLP systems because they
cause tokenization and out-of-vocabulary errors. They can also make the text
less readable, especially in reference printed books, where they are
extensively used. Abbreviations are especially problematic in low-resource
settings, where systems are less robust to begin with. In this paper, we
propose a new method for addressing the problems caused by a high density of
domain-specific abbreviations in a text. We apply this method to the case of a
Slovenian biographical lexicon and evaluate it on a newly developed
gold-standard dataset of 51 Slovenian biographies. Our abbreviation
identification method performs significantly better than commonly used ad-hoc
solutions, especially at identifying unseen abbreviations. We also propose and
present the results of a method for expanding the identified abbreviations in
context.

本文提出了一种新方法，用于处理文字中高密度领域特定缩略语所带来的问题，将此方法应用于斯洛文尼亚传记词典的案例，并在新开发的 51 个斯洛文尼亚传记的黄金标准数据集上进行了评估，提出并展示了一种在上下文中扩展缩略语的方法。此外，该缩略语识别方法表现出显著优异的性能，特别是在识别未出现的缩略语时。