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SMERT: 감성 분석 및 감정 탐지를 위한 단일 입출력 멀티 모달 BERT
김경훈(Kyeonghun Kim),박진욱(Jinuk Park),이지은(Jieun Lee),박상현(Sanghyun Park) Korean Institute of Information Scientists and Eng 2021 정보과학회논문지 Vol.48 No.10
Sentiment Analysis is defined as a task that analyzes subjective opinion or propensity and, Emotion Detection is the task that finds emotions such as ‘happy’ or ‘sad’ from text data. Multimodal data refers to the appearance of image and voice data in addition to text data. In prior research, RNN or cross-transformer models were used, however, RNN models have long-term dependency problems. Also, since cross-transformer models could not capture the attribute of modalities, they got worse results. To solve those problems, we propose SMERT based on a single-stream transformer ran on a single network. SMERT can get joint representation for Sentiment Analysis and Emotion Detection. Besides, we use BERT tasks which are improved to utilize for multimodal data. To present the proposed model, we verify the superiority of SMERT through a comparative experiment on the combination of modalities using the CMU-MOSEI dataset and various evaluation metrics.
이경훈(Kyeonghun Lee),박종술(Jongsool Park),황동환(Donghwan Hwang),이창욱(Changwook Lee),이승호(Seungho Lee),국재창(Jaechang Kook) 한국자동차공학회 2015 한국자동차공학회 학술대회 및 전시회 Vol.2015 No.11
The innovative program to design new structures of multi-speed automatic transmission is proposed with the optimized logics in each process from generating fixed member to implement single transition in shift table. The program solved astronomical number of combinations of planetary gears and clutch members by calculating the performance such like gear efficiency, speed, torque and etc. As a result of the application of the program, this paper shows new multi speed automatic transmission and possibility for the new concept of transmission.