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

2021/09/02 by Eyüp Halit Yılmaz, Eyup Halit Yilmaz, Yilmaz, Eyup Halit +3
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Speech and dialogue systems #Topic Modeling #cs.AI #cs.CL

paper · pdf · doi:10.48550/arxiv.2109.00729

5 pages, 1 figure, conference

arxiv created 2021/09/02 · openalex publication_date 2021/09/02 · arxiv updated 2021/09/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 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.

Citations

• Using Word Embeddings for Automatic Query Expansion
• HuggingFace's Transformers: State-of-the-art Natural Language Processing
• Language Models are Few-Shot Learners
• Query expansion with artificially generated texts
• Making Pre-trained Language Models Better Few-shot Learners

Related

• Few-Shot Query Intent Detection via Relation-Aware Prompt Learning
• QAID: Question Answering Inspired Few-shot Intent Detection
• Efficient Intent Detection with Dual Sentence Encoders
• ConvFiT: Conversational Fine-Tuning of Pretrained Language Models
• User Intent Inference for Web Search and Conversational Agents