  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.
