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Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems
[article]
2019
arXiv
pre-print
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
arXiv:1905.05644v1
fatcat:jv4gkacrevbm7dal3sxycwfpmm