Active learning (AL) is a prominent technique for reducing the annotation
effort required for training machine learning models. Deep learning offers a
solution for several essential obstacles to deploying AL in practice but
introduces many others. One of such problems is the excessive computational
resources required to train an acquisition model and estimate its uncertainty
on instances in the unlabeled pool. We propose two techniques that tackle this
issue for text classification and tagging tasks, offering a substantial
reduction of AL iteration duration and the computational overhead introduced by
deep acquisition models in AL. We also demonstrate that our algorithm that
leverages pseudo-labeling and distilled models overcomes one of the essential
obstacles revealed previously in the literature. Namely, it was shown that due
to differences between an acquisition model used to select instances during AL
and a successor model trained on the labeled data, the benefits of AL can
diminish. We show that our algorithm, despite using a smaller and faster
acquisition model, is capable of training a more expressive successor model
with higher performance.

该研究介绍了两种用于文本分类和标记任务的技术，可以显著减少深度学习中主动学习繁重的计算资源需求，通过伪标签和蒸馏模型实现更高性能的模型训练。

面向可计算的深度主动学习

Towards Computationally Feasible Deep Active Learning

We present a general-purpose tagger based on convolutional neural networks
(CNN), used for both composing word vectors and encoding context information.
The CNN tagger is robust across different tagging tasks: without task-specific
tuning of hyper-parameters, it achieves state-of-the-art results in
part-of-speech tagging, morphological tagging and supertagging. The CNN tagger
is also robust against the out-of-vocabulary problem, it performs well on
artificially unnormalized texts.

使用卷积神经网络的通用标记器（CNN）在不同的标记任务中表现强壮，它能够生成词向量和编码上下文信息，并且没有任务特定的调整能够在词性标注、形态标注和超级标注等任务上达到最优结果，同时它还能很好地解决未登录词问题。

使用卷积神经网络的通用标注器

A General-Purpose Tagger with Convolutional Neural Networks

Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has
been shown to be very effective for modeling and predicting sequential data,
e.g. speech utterances or handwritten documents. In this study, we propose to
use BLSTM-RNN for a unified tagging solution that can be applied to various
tagging tasks including part-of-speech tagging, chunking and named entity
recognition. Instead of exploiting specific features carefully optimized for
each task, our solution only uses one set of task-independent features and
internal representations learnt from unlabeled text for all tasks.Requiring no
task specific knowledge or sophisticated feature engineering, our approach gets
nearly state-of-the-art performance in all these three tagging tasks.

本研究提出了一种基于双向长短期记忆递归神经网络（BLSTM-RNN）的统一标记解决方案，可应用于各种标记任务，包括词性标注、分块和命名实体识别，该方法利用从无标记文本中学习的一组独立任务特征和内部表示，并不需要特定的任务知识或复杂的特征工程，在所有这三个标记任务中获得了几乎最先进的性能。