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Style Transfer for Chat Language using Unsupervised Machine Translation
Youngjun Jung, Changki Lee, Jeongin Hwang, Hyungjong Noh
http://doi.org/10.5626/JOK.2023.50.1.19
Style transfer is the task of generating text of a target style while maintaining content of given text written in a source style. In general, it is assumed that the content is an invariant and the style is variable when the style of the text is transferred. However, in the case of chat language, there is a problem in that it is not well trained by existing style transfer model. In this paper, we proposed a method of transfer chat language into written language using a style transfer model with unsupervised machine translation. This study shows that it is possible to construct a word transfer dictionary between styles that can be used for style transfer by utilizing transferred results. Additionally, it shows that transferred results can be improved by applying a filtering method to transferred result pair so that only well transferred results can be used and by training the style transfer model using a supervised learning method with filtered results.
Noise Injection for Natural Language Sentence Generation from Knowledge Base
http://doi.org/10.5626/JOK.2020.47.10.965
Generating a natural language sentence from Knowledge base is an operation of entering a triple in the Knowledge base to generate triple information, which is a natural language sentence containing the relationship between the entities. To solve the task of generating sentences from triples using a deep neural network, learning data consisting of many pairs of triples and natural language sentences are required. However, it is difficult to learn the model because the learning data composed in Korean is not yet released. To solve the deficiency of learning data, this paper proposes an unsupervised learning method that extracts keywords based on Korean Wikipedia sentence data and generates learning data using a noise injection technique. To evaluate the proposed method, we used gold-standard dataset produced by triples and sentence pairs. Consequently, the proposed noise injection method showed superior performances over normal unsupervised learning on various evaluation metrics including automatic and human evaluations.
Wave Celerity Estimation using Unsupervised Image Registration from Video Imagery
Jinah Kim, Jaeil Kim, Sungwon Shin
http://doi.org/10.5626/JOK.2019.46.12.1296
In this paper, we propose an image registration method based on unsupervised learning to estimate wave celerity by tracking wave movements using a large amount of video imagery. It is difficult to estimate the wave celerity accurately using physics-based modeling in the coastal region, owing to the limitations of in-situ measurement and the high nonlinearity of wave phenomena itself as well as high complexity from nonlinear interactions. In order to estimate wave celerity, the proposed method learns the nonlinear wave behavior from the video imagery. Autoencoder is applied to separate hydrodynamics scenes from environmental factors, such as daylights. The displacement vector of propagating waves is computed by non-linear spatio-temporal image registration. The wave celerity is estimated by accumulating the displacement vectors along time. In this paper, we compare the wave celerity measurement with conventional image processing methods and actual measurement using sensors for accuracy evaluation.
Feature-based Gene Classification and Region Clustering using Gene Expression Grid Data in Mouse Hippocampal Region
Mi-Sun Kang, HyeRyun Kim, Sukchan Lee, Myoung-Hee Kim
Brain gene expression information is closely related to the structural and functional characteristics of the brain. Thus, extensive research has been carried out on the relationship between gene expression patterns and the brain’s structural organization. In this study, Principal Component Analysis was used to extract features of gene expression patterns, and genes were automatically classified by spatial distribution. Voxels were then clustered with classified specific region expressed genes. Finally, we visualized the clustering results for mouse hippocampal region gene expression with the Allen Brain Atlas. This experiment allowed us to classify the region-specific gene expression of the mouse hippocampal region and provided visualization of clustering results and a brain atlas in an integrated manner. This study has the potential to allow neuroscientists to search for experimental groups of genes more quickly and design an effective test according to the new form of data. It is also expected that it will enable the discovery of a more specific sub-region beyond the current known anatomical regions of the brain.
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