Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations

TL;DR

This paper presents a method to generate synthetic hierarchical semantic parsing data using a pretrained BART model, aiming to reduce the need for costly labeled data in complex conversational AI tasks.

Contribution

The authors propose a novel approach combining template extraction, fine-tuning BART, and an auxiliary parser to generate high-quality synthetic data for hierarchical semantic parsing.

Findings

Synthetic data improves parser performance on the Facebook TOP dataset.

The approach reduces reliance on manually labeled hierarchical data.

Filtered synthetic data maintains high quality for training semantic parsers.

Abstract

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 dataset for navigation domain.