Generating synthetic data for task-oriented semantic parsing with hierarchical representations
Modern conversational AI systems support natural language understanding for a wide variety of capabilities. While a majority of these tasks can be accomplished using a simple and ﬂat representation of intents and slots, more sophisticated capabilities require complex hierarchical representations supported by semantic parsing. State-of-the-art semantic parsers are trained using supervised learning with data labeled according to a hierarchical schema which might be costly to obtain or not readily available for a new domain. In this work, we explore the possibility of generating synthetic data for neural semantic parsing using a pretrained denoising sequence-to-sequence model (i.e., BART). Speciﬁcally, we ﬁrst extract masked templates from the existing labeled utterances, and then ﬁne-tune BART to generate synthetic utterances conditioning on the extracted templates. Finally, we use an auxiliary parser (AP) to ﬁlter the generated utterances. The AP guarantees the quality of the generated data. We show the potential of our approach when evaluating on the Facebook TOP dataset1 for navigation domain.