Utilisation of open intent recognition models for customer support intent detection
Businesses have sought out new solutions to provide support and improve customer satisfaction as more products and services have become interconnected digitally. There is an inherent need for businesses to provide or outsource fast, efficient and knowledgeable support to remain competitive. Support solutions are also advancing with technologies, including use of social media, Artificial Intelligence (AI), Machine Learning (ML) and remote device connectivity to better support customers. Customer support operators are trained to utilise these technologies to provide better customer outreach and support for clients in remote areas. Interconnectivity of products and support systems provide businesses with potential international clients to expand their product market and business scale. This paper reports the possible AI applications in customer support, done in collaboration with the Knowledge Transfer Partnership (KTP) program between Birmingham City University and a company that handles customer service systems for businesses outsourcing customer support across a wide variety of business sectors. This study explored several approaches to accurately predict customers' intent using both labelled and unlabelled textual data. While some approaches showed promise in specific datasets, the search for a single, universally applicable approach continues. The development of separate pipelines for intent detection and discovery has led to improved accuracy rates in detecting known intents, while further work is required to improve the accuracy of intent discovery for unknown intents.
Code (0)
등록된 구현이 없습니다.
Tasks
Intent DetectionIntent DiscoveryIntent RecognitionTransfer LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English
Automation of on-call customer support relies heavily on accurate and efficient speech-to-intent (S2I) systems. Building such systems using multi-component pipelines can pose various challenges because they require large…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classificationIntent Classification+2Enhancing Retrieval-Augmented Generation for Electric Power Industry Customer Support
Many AI customer service systems use standard NLP pipelines or finetuned language models, which often fall short on ambiguous, multi-intent, or detail-specific queries. This case study evaluates recent techniques: query …
Intent RecognitionIntent Recognition in Conversational Recommender Systems
Any organization needs to improve their products, services, and processes. In this context, engaging with customers and understanding their journey is essential. Organizations have leveraged various techniques and techno…
ChatbotFeature EngineeringIntent RecognitionQuestion Answering+1From Intent Discovery to Recognition with Topic Modeling and Synthetic Data
Understanding and recognizing customer intents in AI systems is crucial, particularly in domains characterized by short utterances and the cold start problem, where recommender systems must include new products or servic…
DiversityIntent DiscoveryIntent RecognitionRecommendation Systems+1Un analyseur de conversations pour la relation client (Parsing email and chat conversations for customer support softwares)
Cette d{\'e}monstration a pour objet de pr{\'e}senter l{'}utilisation d{'}un analyseur de conversations par email ou chat dans le cadre d{'}une application de support client : mise en valeur des demandes d{'}action, rep{…
SENTER