Dense Template Retrieval for Customer Support

Priberam Labs

Instituto Superior Técnico, Anfiteatro PA2

Abstract:Templated answers are used extensively in customer support scenarios, providing an efficient way to cover a plethora of topics, with an easily maintainable collection of templates. However, the number of templates is often too high for an agent to manually search. Automatically suggesting the correct template for a given question can thus improve the service efficiency, reducing costs and leading to a better customer satisfaction. In this work, we propose a dense retrieval framework for the customer support scenario, adapting a standard in-batch negatives technique to support unpaired sampling of queries and templates. We also propose a novel loss that extends the typical query-centric similarity, exploiting other similarity relations in the training data. Experiments show that our approach achieves considerable improvements, in terms of performance and training speed, over more standard dense retrieval methods. This includes methods such as DPR, and also ablated versions of the proposed approach.Bio:Tiago Mesquita is a Machine Learning Scientist at Zendesk. He holds a master's degree (MSc) in Computer Science and Engineering obtained from Instituto Superior Tecnico in 2021. His current line of research is centered around machine learning methods applied to customer support. Tiago´s other research interests include Natural Language Processing and Computer Vision.<a href="https://www.priberam.com/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">www.priberam.com</a>

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