Transferring knowledge from one domain to another is of practical importance
for many tasks in natural language processing, especially when the amount of
available data in the target domain is limited. In this work, we propose a
novel few-shot approach to domain adaptation in the context of Named Entity
Recognition (NER). We propose a two-step approach consisting of a variable base
module and a template module that leverages the knowledge captured in
pre-trained language models with the help of simple descriptive patterns. Our
approach is simple yet versatile and can be applied in few-shot and zero-shot
settings. Evaluating our lightweight approach across a number of different
datasets shows that it can boost the performance of state-of-the-art baselines
by 2-5% F1-score.

本文提出了一种利用预训练语言模型进行跨领域适应的轻量级的 few-shot 近似算法，将知识从一个领域转移到另一个领域以优化实体识别性能。实验结果表明，与最先进的基线相比，本方法可以提高 2-5% 的 F1-score。

TOKEN 是 MASK：预训练语言模型下的少样本命名实体识别

TOKEN is a MASK: Few-shot Named Entity Recognition with Pre-trained Language Models

We are interested in how to design reinforcement learning agents that
provably reduce the sample complexity for learning new tasks by transferring
knowledge from previously-solved ones. The availability of solutions to related
problems poses a fundamental trade-off: whether to seek policies that are
expected to achieve high (yet sub-optimal) performance in the new task
immediately or whether to seek information to quickly identify an optimal
solution, potentially at the cost of poor initial behavior. In this work, we
focus on the second objective when the agent has access to a generative model
of state-action pairs. First, given a set of solved tasks containing an
approximation of the target one, we design an algorithm that quickly identifies
an accurate solution by seeking the state-action pairs that are most
informative for this purpose. We derive PAC bounds on its sample complexity
which clearly demonstrate the benefits of using this kind of prior knowledge.
Then, we show how to learn these approximate tasks sequentially by reducing our
transfer setting to a hidden Markov model and employing spectral methods to
recover its parameters. Finally, we empirically verify our theoretical findings
in simple simulated domains.

本研究旨在探讨如何设计强化学习代理，通过从之前解决的任务中转移知识，明确减少学习新任务的样本复杂度。具体地，本文关注第二种目标，即当代理具有状态行为对的生成模型时，如何快速识别最精确的解法。我们将转移设置降至一个隐马尔可夫模型，并使用谱方法从中恢复其参数。最后，我们在简单的模拟领域中实证了我们的理论发现。