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 into multi-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.
Highlights
Natural language understanding (NLU) and Speech understanding (SU) play a significantly important role in human-computer interaction (HCI) applications
This study reduces human efforts for manual annotation of utterances by incorporating unannotated datasets from various data sources, such as user query logs into a deep neural network (DNN) algorithm, i.e., a single jointly trained long short-term memory (LSTM) based NLU model of a multi-domain intelligent chatbot
We proposed a semi-supervised joint model, SEMI-MDJM, for intelligent chatbot system to extract the domain, intent, and entity of user queries using a single model machine learning (ML) model based on LSTM to mitigate the propagation of downstream error
Summary
Natural language understanding (NLU) and Speech understanding (SU) play a significantly important role in human-computer interaction (HCI) applications. Organizations are struggling to manage the growth of such user query data They have been implementing intelligent chatbot to provide service to customers 24/7 with or without call center help to address these issues. Such intelligent systems have three most important parts: domain classification, intent detection, and entity recognition. This study reduces human efforts for manual annotation of utterances by incorporating unannotated datasets from various data sources, such as user query logs into a DNN algorithm, i.e., a single jointly trained long short-term memory (LSTM) based NLU model of a multi-domain intelligent chatbot. A single semi-supervised multi-domain joint model (SEMI-MDJM) based on LSTM outperforms a joint base model and an adversarial multi-domain joint model in each task i.e., domain classification, intent prediction, and entity recognition
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