Privacy-Preserving Domain Adaptation of Semantic Parsers

To mitigate that problem, Differentially Private In task-oriented dialogue systems, such as Siri and (DP) training algorithms, such as DP-SGD (Abadi Alexa, a software agent parses a user's intent into et al., 2016; Dwork et al., 2006), can be used to a program, executes it and then communicates the provide worst-case guarantees on the information results back to the user (Andreas et al., 2020; Li leakage of a trained model. This guarantee is et al., 2022; Cheng et al., 2020; Gupta et al., 2018; controlled by the privacy budget ϵ, where lower Young et al., 2013). As a result of their growing epsilon means higher privacy. But while DP-SGD popularity, these systems face an increasing could be used to adapt (fine-tune) a semantic parser demand to improve their linguistic coverage (How on unannotated private data, there is a limit to what do users talk?) as well as functional coverage can be done in this way. Even if some users are (What are users trying to do?). An input utterance asking the system to hop up and down, fine-tuning to such a system could look like this: "Could you is unlikely to make it grow legs.