2024
DOI: 10.18510/hssr.2024.12214
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Dataset Expansion with Pseudo-Labeling: Case Study for Optimizing Chatbot Intent Recognition

Karolina Kuligowska,
Bartłomiej Kowalczuk

Abstract: Purpose of the study: This study presents an approach for improving the performance of natural language processing (NLP) models in pseudo-labeling tasks, with a particular focus on enhancing chatbot model intent recognition for business use cases. Methodology: The employed case study approach explores the pseudo-labeling technique and demonstrates a practical and efficient way to iteratively expand the original set of labeled data for the purpose of refining model training to achieve superior intent recogniti… Show more

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