# Intent Classification and Slot Filling Model for In-Vehicle Services in Korean
## 1 Introduction  
Along with the increasing significance of artificial intelligence (AI) speakers, their understanding capability of a user's request becomes a critical task. In order to build a smart AI speaker such as Google Home or Amazon Echo, natural language understanding (NLU) is an important component to accomplish the goal through human interaction. In specific, NLU in a task-oriented spoken dialogue system typically is divided into intent classification and slot filling [1]. Intent classification is a task to distinguish the intention of the dialogue, slot filling finds the correct value for the corresponding slots [2]. Unlike previous studies that focus on developing independent models for each task [3; 4; 5], recent studies on task-oriented dialogue systems introduce the models that jointly learn diverse tasks simultaneously [6; 7; 8; 9; 1]. The models built with joint learning on intent classification and slot filling in English show improved performance on the task-oriented dialogue datasets as well [8; 9]. The datasets for the task-oriented dialogue system include diverse domains such as booking airlines, restaurants, and hotels or playing music [10; 11; 12; 13; 14]. Despite the substantial performance of joint models, the above models assume users at home or work so that they cannot cover the users who are in a specific environment like in-vehicle. Since in-vehicle services usually maintain distinct intents and slots compared to normal dialogue at home, it is critical to consolidate the robustness of the model with car-adaptive data. Moreover, they are built with the assumption that the user's request is limited to a small number of intents, such as \"find\", or \"book\" compared to our expectation from in-vehicle AI speakers. To avoid the limited coverage and the small number of intents of in-vehicle AI speaker services in English, the studies that consider the users who are under the condition of in-vehicle appear as well [15; 16; 17]. On the contrary, Korean language models still suffer from the narrow range of task-oriented dialogue data to build in-vehicle AI speaker service. Even though the datasets for task-oriented dialogue are available ([[https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=71265](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=71265)]([https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=71265](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=71265)), accessed on 3 November 2022) [18; 19; 20], the dataset that covers in-vehicle situation does not exist. The limited number of datasets leads to the absence of models that learn intent classification and slot filling in a multi-tasking manner. In this paper, we introduce a model that captures the suitable intents and finds slot value for proper slots simultaneously with the Korean in-vehicle services dataset. Our model encodes the user's utterance with a pre-trained language model to find the correct intent of the request. Then, the model finds the proper slots and fills them with predicted slot values. In the end, the value-refiner post-processes the slot value with database-matching module and value-matching module for precise slot values. From the experiments, we find that our model show improved performances on in-vehicle services in Korean. We also conduct qualitative analysis for detailed investigation of regarding intents and slots. Moreover, we show an ablation study to show the effectiveness of the value-refiner with two database-matching module and value matching module. * We propose a model that learns in-vehicle services situations with diverse domains in Korean that are jointly trained with intent classification and slot-filling. * To show our model's effectiveness, we conduct experiments on a mobility domain dataset and show comparable performances on the dataset.