Understanding what the users say in chatbots: A case study for the Vietnamese language
Authors: Luong Chi Tho, Tran Thi Oanh
Abstract: This paper1 presents a study on understanding what the users say in chatbot systems: the situation where users input utterances bots would hopefully detect intents and recognize corresponding contexts implied by utterances. This helps bots better understand what users are saying, and act upon a much wider range of actions. To this end, we propose a framework which models the first task as a classification problem and the second one as a two-layer sequence labeling problem. The framework explores deep neural networks to automatically learn useful features at both character and word levels. We apply this framework to building a chatbot in a Vietnamese e-commerce domain to help retail brands better communicate with their customers. Experimental results on four newly-built datasets demonstrate that deep neural networks could be able to outperform strong conventional machine-learning methods. In detecting intents, we achieve the best F-measure of 82.32%. In extracting contexts, the proposed method yields promising F-measures ranging from 78% to 91% depending on specific types of contexts.
Published: 11 November 2019