The impact of semi-supervised learning on the performance of intelligent chatbot system

Abstract

Artificial intelligent based dialog systems are getting attention from both business and academic communities. The key parts for such intelligent chatbot systems are domain classification, intent detection, and named entity recognition. Various supervised, unsupervised, and hybrid approaches are used to detect each field. Such intelligent systems, also called natural language understanding systems analyze user requests in sequential order: Domain classification, intent, and entity recognition based on the semantic rules of the classified domain. This sequential approach propagates the downstream error; i.e., if the domain classification model fails to classify the domain, intent and entity recognition fail. Furthermore, training such intelligent system necessitates a large number of user-annotated datasets for each domain. This study proposes a single joint predictive deep neural network framework based on long short-term memory using only a small user-annotated dataset to address these issues. It investigates value added by incorporating unlabeled data from user chatting logs intomulti-domain spoken language understanding systems. Systematic experimental analysis of the proposed joint frameworks, along with the semi-supervised multi-domain model, using open-source annotated and unannotated utterances shows robust improvement in the predictive performance of the proposed multi-domain intelligent chatbot over a base joint model and joint model based on adversarial learning.