ConQX: Semantic Expansion of Spoken Queries for Intent Detection based on Conditioned Text Generation

Anonymous

Keywords: BERT, GPT2, Query Expansion, Prompt Engineering, Prompt Tuning, Text Generation

TL;DR: We propose a method for conditional semantic expansion of spoken queries, called ConQX, which utilizes the text generation ability of GPT-2 for improving Intent Detection performance.

Abstract: 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 conditional 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.