While deep learning is a powerful tool for natural language processing (NLP)
problems, successful solutions to these problems rely heavily on large amounts
of annotated samples. However, manually annotating data is expensive and
time-consuming. Active Learning (AL) strategies reduce the need for huge
volumes of labeled data by iteratively selecting a small number of examples for
manual annotation based on their estimated utility in training the given model.
In this paper, we argue that since AL strategies choose examples independently,
they may potentially select similar examples, all of which may not contribute
significantly to the learning process. Our proposed approach,
Active$\mathbf{^2}$ Learning (A$\mathbf{^2}$L), actively adapts to the deep
learning model being trained to eliminate such redundant examples chosen by an
AL strategy. We show that A$\mathbf{^2}$L is widely applicable by using it in
conjunction with several different AL strategies and NLP tasks. We empirically
demonstrate that the proposed approach is further able to reduce the data
requirements of state-of-the-art AL strategies by $\approx \mathbf{3-25\%}$ on
an absolute scale on multiple NLP tasks while achieving the same performance
with virtually no additional computation overhead.

本文介绍了一种名为 Active² Learning 的主动学习策略，该策略可与多种 AL 策略和 NLP 任务结合使用，并能减少 3-25％的数据要求而无需额外计算开销。

主动二次学习：在序列标注和机器翻译的主动学习方法中主动减少冗余

Active$^2$ Learning: Actively reducing redundancies in Active Learning  methods for Sequence Tagging and Machine Translation

Active learning (AL) is a widely-used training strategy for maximizing
predictive performance subject to a fixed annotation budget. In AL one
iteratively selects training examples for annotation, often those for which the
current model is most uncertain (by some measure). The hope is that active
sampling leads to better performance than would be achieved under independent
and identically distributed (i.i.d.) random samples. While AL has shown promise
in retrospective evaluations, these studies often ignore practical obstacles to
its use. In this paper we show that while AL may provide benefits when used
with specific models and for particular domains, the benefits of current
approaches do not generalize reliably across models and tasks. This is
problematic because in practice one does not have the opportunity to explore
and compare alternative AL strategies. Moreover, AL couples the training
dataset with the model used to guide its acquisition. We find that subsequently
training a successor model with an actively-acquired dataset does not
consistently outperform training on i.i.d. sampled data. Our findings raise the
question of whether the downsides inherent to AL are worth the modest and
inconsistent performance gains it tends to afford.

本文研究了主题 “主动学习”，发现尽管在特定模型和特定领域中使用主动学习可能会带来好处，但在不同模型和任务之间泛化当前方法的好处不可靠，并且主动学习的认购数据与指导其获取的模型相结合，与使用独立同分布的（I.I.D）随机样本训练继任模型的方法相比并没有明显的优势，主动学习的局限性是否值得现实中的应用，值得深思。