Enhancing Intent Detection in Customer Service with Social Media Data

Abstract

Intent detection plays an important role in customer service dialog systems for providing high-quality service in the financial industry. The lack of publicly available datasets and high annotation cost are two challenging issues in this research direction. To overcome these challenges, we propose a social media enhanced self-training approach for intent detection by using label names only. The experimental results show the effectiveness of the proposed method.