Intent detection of spoken queries is a challenging task due to their noisy structure and short length. To provide additional information regarding the query and enhance the performance of intent detection, we propose a method for semantic expansion of spoken queries, called ConQX, which utilizes the text generation ability of an auto-regressive language model, GPT-2. To avoid off-topic text generation, we condition the input query to a structured context with prompt mining. We then apply zero-shot, one-shot, and few-shot learning. We lastly use the expanded queries to fine-tune BERT and RoBERTa for intent detection. The experimental results show that the performance of intent detection can be improved by our semantic expansion method.
@article{arxiv.2109.00729,
title = {ConQX: Semantic Expansion of Spoken Queries for Intent Detection based on Conditioned Text Generation},
author = {Eyup Halit Yilmaz and Cagri Toraman},
journal= {arXiv preprint arXiv:2109.00729},
year = {2021}
}