Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation

TL;DR

This paper proposes a data augmentation method to improve low-resource semantic parsing by generating structured utterances and simulating natural language, achieving significant performance gains under realistic constraints.

Contribution

It introduces a novel data augmentation approach tailored for low-resource, privacy-sensitive semantic parsing scenarios with no reliance on related datasets or direct grammar sampling.

Findings

33% relative improvement in top-1 match on SMCalFlow dataset

Effective data augmentation despite restrictive real-world constraints

Demonstrates viability of structured utterance generation for low-resource parsing

Abstract

We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datasets/models from a related domain, (2) inability to sample useful logical forms directly from a grammar, and (3) privacy requirements for unlabeled natural utterances. Our goal is to improve a low-resource semantic parser using utterances collected through user interactions. In this highly challenging but realistic setting, we investigate data augmentation approaches involving generating a set of structured canonical utterances corresponding to logical forms, before simulating corresponding natural language and filtering the resulting pairs. We find that such approaches are effective despite our restrictive setup: in a low-resource setting on the complex SMCalFlow calendaring dataset (Andreas et al., 2020), we observe 33% relative improvement over a non-data-augmented baseline in top-1 match.