Meta-Learning for Low-resource Natural Language Generation in Task-oriented
Dialogue Systems
Abstract
Natural language generation (NLG) is an essential
component of task-oriented dialogue systems. Despite the recent success of neural approaches for
NLG, they are typically developed for particular
domains with rich annotated training examples. In
this paper, we study NLG in a low-resource setting
to generate sentences in new scenarios with handful
training examples. We formulate the problem from
a meta-learning perspective, and propose a generalized optimization-based approach (Meta-NLG)
based on the well-recognized model-agnostic metalearning (MAML) algorithm. Meta-NLG defines a
set of meta tasks, and directly incorporates the objective of adapting to new low-resource NLG tasks
into the meta-learning optimization process. Extensive experiments are conducted on a large multidomain dataset (MultiWoz) with diverse linguistic
variations. We show that Meta-NLG significantly
outperforms other training procedures in various
low-resource configurations. We analyze the results, and demonstrate that Meta-NLG adapts extremely fast and well to low-resource situations