Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems

Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems

Fei Mi, Minlie Huang, Jiyong Zhang, Boi Faltings

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 3151-3157. https://doi.org/10.24963/ijcai.2019/437

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 meta-learning (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 multi-domain dataset (MultiWoz) with diverse linguistic variations. We show that Meta-NLGĀ significantly outperforms other training procedures in various low-resource configurations. We analyze the results, and demonstrate that Meta-NLGĀ adapts extremely fast and well to low-resource situations.
Keywords:
Machine Learning: Transfer, Adaptation, Multi-task Learning
Natural Language Processing: Natural Language Generation
Natural Language Processing: Dialogue