Despite recent advancements in tabular language model research, real-world
applications are still challenging. In industry, there is an abundance of
tables found in spreadsheets, but acquisition of substantial amounts of labels
is expensive, since only experts can annotate the often highly technical and
domain-specific tables. Active learning could potentially reduce labeling
costs, however, so far there are no works related to active learning in
conjunction with tabular language models. In this paper we investigate
different acquisition functions in a real-world industrial tabular language
model use case for sub-cell named entity recognition. Our results show that
cell-level acquisition functions with built-in diversity can significantly
reduce the labeling effort, while enforced table diversity is detrimental. We
further see open fundamental questions concerning computational efficiency and
the perspective of human annotators.

该研究探讨了在实际工业中使用表格语言模型的情景下，使用不同获取函数进行子单元命名实体识别的主题并发现 cell-level 获取函数具有内置多样性，可以显着降低标注工作量。同时还发现了存在人工标注方面的计算效率问题和未解决的问题。

基于表格语言模型的主动学习

Active Learning with Tabular Language Models

Active learning is a paradigm aimed at reducing the annotation effort by
training the model on actively selected informative and/or representative
samples. Another paradigm to reduce the annotation effort is self-training that
learns from a large amount of unlabeled data in an unsupervised way and
fine-tunes on few labeled samples. Recent developments in self-training have
achieved very impressive results rivaling supervised learning on some datasets.
The current work focuses on whether the two paradigms can benefit from each
other. We studied object recognition datasets including CIFAR10, CIFAR100 and
Tiny ImageNet with several labeling budgets for the evaluations. Our
experiments reveal that self-training is remarkably more efficient than active
learning at reducing the labeling effort, that for a low labeling budget,
active learning offers no benefit to self-training, and finally that the
combination of active learning and self-training is fruitful when the labeling
budget is high. The performance gap between active learning trained either with
self-training or from scratch diminishes as we approach to the point where
almost half of the dataset is labeled.

本研究针对减少标注工作的两种范式：主动学习和自学习，研究它们能否相互受益。在对象识别数据集（包括 CIFAR10、CIFAR100 和 Tiny ImageNet）上的实验证明：对于低的标注预算，主动学习对自学习没有帮助。当标注预算很高时，主动学习和自学习的组合是有益的。

减少标注工作量：自监督活跃学习相遇

Reducing Label Effort: Self-Supervised meets Active Learning

We design an active learning algorithm for cost-sensitive multiclass
classification: problems where different errors have different costs. Our
algorithm, COAL, makes predictions by regressing to each label's cost and
predicting the smallest. On a new example, it uses a set of regressors that
perform well on past data to estimate possible costs for each label. It queries
only the labels that could be the best, ignoring the sure losers. We prove COAL
can be efficiently implemented for any regression family that admits squared
loss optimization; it also enjoys strong guarantees with respect to predictive
performance and labeling effort. We empirically compare COAL to passive
learning and several active learning baselines, showing significant
improvements in labeling effort and test cost on real-world datasets.

我们针对多分类成本敏感分类问题设计了主动学习算法：通过回归每个标签的成本并预测最小值来进行预测，它只查询可能最好的标签，忽略不可能的失败者，我们证明了 COAL 可以高效地实现任何支持平方损失优化的回归函数家族；它还享有与预测性能和标签工作量相关的强大保证。我们在真实数据集上将 COAL 与被 动学习和几种主动学习基线进行了经验比较，证明在标签工作量和测试成本方面有显着的改进。