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

• URL: http://arxiv.org/abs/2109.00729v1
• Date: Thu, 2 Sep 2021 05:57:07 GMT
• Title: ConQX: Semantic Expansion of Spoken Queries for Intent Detection based on Conditioned Text Generation
• Authors: Eyup Halit Yilmaz and Cagri Toraman
• Abstract summary: We propose a method for semantic expansion of spoken queries, called ConQX. 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 to fine-tune BERT and RoBERTa for intent detection.
• Score: 4.264192013842096
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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