Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing
Abstract
Task-oriented semantic parsing is a critical component of virtual assistants, which is responsible for understanding the user’s intents (set reminder, play music, etc.). Recent advances in deep learning have enabled several approaches to successfully parse more complex queries (Gupta et al., 2018; Rongali et al., 2020), but these models require a large amount of annotated training data to parse queries on new domains (e.g. reminder, music).
In this paper, we focus on adapting task-oriented semantic parsers to low-resource domains, and propose a novel method that outperforms a supervised neural model at a 10-fold data reduction. In particular, we