Augmenting text for spoken language understanding with Large Language
  Models
Roshan Sharma Suyoun Kim Daniel Lazar Trang Le Akshat Shrivastava Kwanghoon Ahn Piyush Kansal Leda Sari Ozlem Kalinli Michael Seltzer
Spoken semantic parsing (SSP) involves generating machine-comprehensible
parses from input speech. Training robust models for existing application
domains represented in training data or extending to new domains requires
corresponding triplets of speech-transcript-semantic parse data, which is
expensive to obtain. In this paper, we address this challenge by examining
methods that can use transcript-semantic parse data (unpaired text) without
corresponding speech. First, when unpaired text is drawn from existing textual
corpora, Joint Audio Text (JAT) and Text-to-Speech (TTS) are compared as ways
to generate speech representations for unpaired text. Experiments on the STOP
dataset show that unpaired text from existing and new domains improves
performance by 2% and 30% in absolute Exact Match (EM) respectively. Second, we
consider the setting when unpaired text is not available in existing textual
corpora. We propose to prompt Large Language Models (LLMs) to generate unpaired
text for existing and new domains. Experiments show that examples and words
that co-occur with intents can be used to generate unpaired text with Llama
2.0. Using the generated text with JAT and TTS for spoken semantic parsing
improves EM on STOP by 1.4% and 2.6% absolute for existing and new domains
respectively.