We study few-shot learning in natural language domains. Compared to many
existing works that apply either metric-based or optimization-based
meta-learning to image domain with low inter-task variance, we consider a more
realistic setting, where tasks are diverse. However, it imposes tremendous
difficulties to existing state-of-the-art metric-based algorithms since a
single metric is insufficient to capture complex task variations in natural
language domain. To alleviate the problem, we propose an adaptive metric
learning approach that automatically determines the best weighted combination
from a set of metrics obtained from meta-training tasks for a newly seen
few-shot task. Extensive quantitative evaluations on real-world sentiment
analysis and dialog intent classification datasets demonstrate that the
proposed method performs favorably against state-of-the-art few shot learning
algorithms in terms of predictive accuracy. We make our code and data available
for further study.

提出了一种自适应度量学习方法，该方法能够从元训练任务中获得一组评估度量，并自动确定最佳加权组合，以捕捉自然语言领域中新的 few-shot 任务的复杂任务变化。在真实情感分析和对话意图分类数据集上进行了广泛的定量评估，结果表明所提出的方法在预测准确性方面表现优于现有的 few shot learning 算法。