Search : [ author: Gyu-Min Park ] (2)

Post-training Methods for Improving Korean Document Summarization Model

So-Eon Kim, Seong-Eun Hong, Gyu-Min Park, Choong Seon Hong, Seong-Bae Park

http://doi.org/10.5626/JOK.2023.50.10.882

The document summarization task generates a short summary based on a long document. Recently, a method using a pre-trained model based on a transformer model showed high performance. However, as it was proved that fine-tuning does not train the model optimally due to the learning gap between pre-training and fine-tuning, post-training, which is additional training between pre-training and fine-tuning, was proposed. This paper proposed two post-training methods for Korean document summarization. One was Korean Spacing, which is for learning Korean structure, and the other was First Sentence Masking, which is for learning about document summarization. Experiments proved that the proposed post-training methods were effective as performance improved when the proposed post-training was used compared to when it was not.

Confident Multiple Choice Learning-based Ensemble Model for Video Question-Answering

Gyu-Min Park, A-Yeong Kim, Seong-Bae Park

http://doi.org/10.5626/JOK.2022.49.4.284

The task of Video Question Answering(VQA) focuses on finding an answer to a question about the given video. VQA models should be able to process the multi-modal information and time-series information in the video in order to answer the questions appropriately. However, designing a model that answers all types of questions robustly is a challenging problem and takes a lot of time. Since the method of combining existing proposed models has different viewpoints of representing video by each model, ensemble models and ensemble learning methods that can reflect each model"s viewpoints are essential to improve the performance. This paper proposes an ensemble model for VQA with Confident Multiple Choice Learning(CMCL) to improve the performance on accuracy. Our experiment shows that the proposed model outperforms other VQA models and ensemble learning methods on the DramaQA dataset. We analyze the impact of the ensemble learning methods on each model.


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