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

Author:

Lim JungwooORCID,Son Suhyune,Lee Songeun,Chun Changwoo,Park Sungsoo,Hur YunaORCID,Lim Heuiseok

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.

Funder

Hyundai Motor Company and Kia

Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education

MSI

ITRC

IITP

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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