State of the Art in Intent Detection and Slot Filling for Question Answering System: A Systematic Literature Review

Abstract

A Question Answering System (QAS), also known as a chatbot, is a Natural Language Processing (NLP) application that automatically provides accurate responses to questions posed by humans in natural language. Intent Detection and Classification are crucial elements in NLP, especially in a taskoriented dialogue system. In this paper, we conduct a systematic literature review that will perform a comparative analysis of different techniques or algorithms that are being implemented for intent detection and classification with slot filling. The goals of this paper are to identify the distribution, methodology, techniques or algorithms, and evaluation methods, that can be used to develop and construct a model of intent detection and classification with slot filling. This paper also reviews academic documents that have been published from 2019 to 2023, based on a four-step selection process of identification, screening, eligibility, and inclusion, for the selection process. In order to examine these documents, a systematic review was conducted and four main research questions were answered. The results discuss the methodology that can be used for the implementation of intent detection and classification with slot filling, along with the techniques, algorithms and evaluation methods that are widely used and currently implemented by other researchers.