以遷移學習改善深度神經網路模型於中文歌詞情緒辨識

Using Transfer Learning to Improve Deep Neural Networks for Lyrics Emotion Recognition in Chinese

情緒是音樂資訊檢索中的重要屬性，目前深度學習方法已被廣泛用於自動音樂情緒辨識。音樂情緒辨識主要以歌曲情緒為主，部分研究關注英文歌詞，罕見對於中文歌詞情緒辨識的研究。本研究提出運用遷移式學習改善深度神經網路模型─BERT 預訓練模型在中文歌詞的情緒分類任務上。實驗結果顯示，直接使用 BERT 對中文維度情緒語料庫建立中文情緒分類模型，對中文歌詞情緒分類僅有 50%的準確度，若使用 BERT 對中文維度情緒字典與片語建立情緒分類模型再遷移至中文維度情緒語料庫，能達到 71%的歌詞情緒分類準確度。

"Emotion is an important attribute in music information retrieval. Deep learning methods have been widely used in the automatic recognition of music emotion. Most of the studies focus on the audio data, the role of lyrics in music emotion classification remains under-appreciated. Due to the richness of English language resources, most previous studies were based on English lyrics but rarely in Chinese. This study proposes an approach without specific training for the Chinese lyrics emotional classification task: using transfer learning to improve deep neural networks, BERT pre-training model, for the emotion classification in Chinese lyrics. The experimental results show that directly using BERT to build an emotion classification model of CVAT only reach 50% of the classification accuracy. However, using BERT with transfer learning from CVAW, CVAP, to CVAT can achieve 71% classification accuracy. "