Model-agnostic meta-learning (MAML) has been recently put forth as a strategy
to learn resource-poor languages in a sample-efficient fashion. Nevertheless,
the properties of these languages are often not well represented by those
available during training. Hence, we argue that the i.i.d. assumption ingrained
in MAML makes it ill-suited for cross-lingual NLP. In fact, under a
decision-theoretic framework, MAML can be interpreted as minimising the
expected risk across training languages (with a uniform prior), which is known
as Bayes criterion. To increase its robustness to outlier languages, we create
two variants of MAML based on alternative criteria: Minimax MAML reduces the
maximum risk across languages, while Neyman-Pearson MAML constrains the risk in
each language to a maximum threshold. Both criteria constitute fully
differentiable two-player games. In light of this, we propose a new adaptive
optimiser solving for a local approximation to their Nash equilibrium. We
evaluate both model variants on two popular NLP tasks, part-of-speech tagging
and question answering. We report gains for their average and minimum
performance across low-resource languages in zero- and few-shot settings,
compared to joint multi-source transfer and vanilla MAML.

为提高 MAML 方法在跨语言自然语言处理中的鲁棒性，本文提出了两个基于最小化最大风险和约束性分布分别的构思而实现的改进版本：Minimax MAML 和 Neyman-Pearson MAML。这两种标准均构成了可微分的两人博弈。研究者在 POSM 和 QA 两个自然语言处理任务中对比了它们与其他方法的效果表现。

异常语言的极小极大和 Neyman-Pearson 元学习

Minimax and Neyman-Pearson Meta-Learning for Outlier Languages

Decisions made by machine learning systems have increasing influence on the
world, yet it is common for machine learning algorithms to assume that no such
influence exists. An example is the use of the i.i.d. assumption in content
recommendation. In fact, the (choice of) content displayed can change users'
perceptions and preferences, or even drive them away, causing a shift in the
distribution of users. We introduce the term auto-induced distributional shift
(ADS) to describe the phenomenon of an algorithm causing a change in the
distribution of its own inputs. Our goal is to ensure that machine learning
systems do not leverage ADS to increase performance when doing so could be
undesirable. We demonstrate that changes to the learning algorithm, such as the
introduction of meta-learning, can cause hidden incentives for auto-induced
distributional shift (HI-ADS) to be revealed. To address this issue, we
introduce `unit tests' and a mitigation strategy for HI-ADS, as well as a toy
environment for modelling real-world issues with HI-ADS in content
recommendation, where we demonstrate that strong meta-learners achieve gains in
performance via ADS. We show meta-learning and Q-learning both sometimes fail
unit tests, but pass when using our mitigation strategy.

机器学习算法的自动分布转移现象 (auto-induced distributional shift, ADS) 对用户的接收内容和对其产生的影响有着不可忽视的影响，研究者设计的 unit tests 和 meta-learning 等策略可避免算法受到 ADS 的影响并取得更好的表现。