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SMERT: Single-stream Multimodal BERT for Sentiment Analysis and Emotion Detection
Kyeonghun Kim, Jinuk Park, Jieun Lee, Sanghyun Park
http://doi.org/10.5626/JOK.2021.48.10.1122
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.
Denoising Multivariate Time Series Modeling for Multi-step Time Series Prediction
Jungsoo Hong, Jinuk Park, Jieun Lee, Kyeonghun Kim, Seung-Kyun Hong, Sanghyun Park
http://doi.org/10.5626/JOK.2021.48.8.892
The research field of time series forecasting predicts the future time point using seasonality in time series. In the industrial environment, since decision-making through continuous perspective prediction of the future is important, multi-step time series forecasting is necessary. However, multi-step prediction is highly unstable because of its dependency on predicted value of previous time prediction result. Therefore, the traditional time series forecasting makes a statistical prediction for the single time point. To address this limitation, we propose a novel encoder-decoder based neural network named ‘DTSNet’ which predicts multi-step time points for multivariate time series. To stabilize multi-step prediction, we exploit positional encoding to enhance representation for time point and propose a novel denoising training method. Moreover, we propose dual attention to resolve long-term dependencies and modeling complex patterns in time series, and we adopt multi-head strategy at linear projection layer for variable-specific modeling. To verify the performance improvement of our approach, we compare and analyze it with baseline models, and we demonstrate the proposed methods through comparison tests, such as, component ablation study and denoising degree experiment.
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