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A Study on Using Transfer Learning to Improve BERT Model for Emotional Classification of Chinese Lyrics.

, , , and . ROCLING, page 13-17. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), (2021)

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Using a self-organizing neural network for wafer defect inspection., , and . SMC (5), page 4312-4317. IEEE, (2004)基於音段式LMR 對映之語音轉換方法的改進 (Improving of Segmental LMR-Mapping Based Voice Conversion Methods) In Chinese., and . ROCLING, Association for Computational Linguistics and Chinese Language Processing (ACLCLP), Taiwan, (2013)A Study on Using Transfer Learning to Improve BERT Model for Emotional Classification of Chinese Lyrics., , , and . ROCLING, page 13-17. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), (2021)A Study on Using Different Audio Lengths in Transfer Learning for Improving Chainsaw Sound Recognition., and . ROCLING, page 67-74. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), (2022)An Unsupervised Self-Organizing Neural Network for Automatic Semiconductor Wafer Defect Inspection., , and . ICRA, page 3000-3005. IEEE, (2005)Impact of Using Creative Thinking Skills and Open Data on Programming Design in a Computer-Supported Collaborative Learning Environment., , , , and . ICALT, page 396-400. IEEE Computer Society, (2016)User Intention-Based Document Summarization on Heterogeneous Sentence Networks., , and . DASFAA (2), volume 11447 of Lecture Notes in Computer Science, page 572-587. Springer, (2019)以線性多變量迴歸來對映分段後音框之語音轉換方法 (A Voice Conversion Method Mapping Segmented Frames with Linear Multivariate Regression) In Chinese., , and . ROCLING, Association for Computational Linguistics and Chinese Language Processing (ACLCLP), Taiwan, (2012)An unsupervised neural network approach for automatic semiconductor wafer defect inspection., , , and . Expert Syst. Appl., 36 (1): 950-958 (2009)A Cluster-based Personalized Item Recommended Approach on the Educational Assessment System., , , and . Int. J. Emerg. Technol. Learn., 10 (5): 52-58 (2015)