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

Jungwoo Lim, Suhyune Son, Songeun Lee, Changwoo Chun, Sungsoo Park, Yuna Hur, Heuiseok Lim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Article number12438
JournalApplied Sciences (Switzerland)
Issue number23
Publication statusPublished - 2022 Dec

Bibliographical note

Publisher Copyright:
© 2022 by the authors.


  • Korean dialogue system
  • in-vehicle services domain
  • intent classification
  • slot filling

ASJC Scopus subject areas

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


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