Huang JLiou Y.-RHSIN-HSI CHEN2021-09-022021-09-022021https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107675583&doi=10.1145%2f3442442.3451377&partnerID=40&md5=d09357c474e542e7dd70024cdfe50ee1https://scholars.lib.ntu.edu.tw/handle/123456789/581358Intent 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. ? 2021 ACM.Social networking (online); World Wide Web; Customer services; Dialog systems; Financial industry; High quality service; Intent detection; Self-training approaches; Social media; Social media datum; Service industryconference paper10.1145/3442442.34513772-s2.0-85107675583