Understanding what the users say in chatbots: A case study for the Vietnamese language. (January 2020) - Tran, Oanh Thi; Luong, Tho Chi
Abstract: This paper 1 presents a study on understanding what the users say in chatbot systems: the situation where users input utterances bots would hopefully (1) detect intents and (2) 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. Highlights: We have developed a framework to deeply analyze user utterances in Vietnamese, which includes two key tasks: an intent parser and a context extractor. We have constructed new annotated corpora for these two tasks: one corpus is about user intents and the other three corpora are about three typical types of contexts existing in ordering chatbots. We show through extensive experiments on these corpora that using automatically learnt features via deep learning networks is quite effective and yields better performance than using hand-crafted ones for the both two tasks.