Intent Classification and Slot Filling Model for In-Vehicle Services in Korean

Abstract

Since understanding a user’s request has become a critical task for the artificial intelligence speakers, capturing intents and finding correct slots along with corresponding slot value is significant. Despite various studies concentrating on a real-life situation, dialogue system that is adaptive to in-vehicle services are limited. Moreover, the Korean dialogue system specialized in an vehicle domain rarely exists. We propose a dialogue system that captures proper intent and activated slots for Korean in-vehicle services in a multi-tasking manner. We implement our model with a pre-trained language model, and it includes an intent classifier, slot classifier, slot value predictor, and value-refiner. We conduct the experiments on the Korean in-vehicle services dataset and show 90.74% of joint goal accuracy. Also, we analyze the efficacy of each component of our model and inspect the prediction results with qualitative analysis.

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

“…In addition, each work's dataset, domain, and language used for the implementation are also written in Table VIII whereby most of the works are developed in English. Therefore, few papers have developed this topic on lowresource languages other than English, such as Bengali [20,31], Chinese [22], Hindi [20,31], Indonesian [19], Italian [23], Korean [30], Tamil [29] and Vietnamese [1].…”

“…Furthermore, many previous research articles have been published and have discussed the researcher's implementation on the topic of ID and IC with slot filling. Papers [1,8,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37] have discussed and presented their approach or implementation on developing ID and IC with slot filling using various frameworks, methodologies, and techniques. Various frameworks, methodologies, approaches, techniques and algorithms have been implemented by these researchers, whereas for instance, [19,22,24,26,27,28,31,34,35,36] have implemented techniques of Bidirectional Long Short-Term Memory ID (BiLSTM) with Conditional Random Forest (CRF) in their implementation of ID and IC with slot filling.…”