相較於其他自然語言處理任務，歌詞生成仍鮮少被研究。而當前最尖端的中文語言模型BERT（雙向編碼轉換器）能將成功捕捉語句間詞彙的語意，並且經過大量語料訓練後在克漏字和預測下一句兩個任務中達到令人驚豔的正確率。因此，我們決定借用其能力填詞。我們提出自動判別歌詞分段的演算法以產生歌詞模板，讓BERT大量學習中文歌詞後，在維持原曲架構和詞句長度的情況下以取代字詞和預測上下句的組合策略創作新歌詞。最後，以BLEU分數和問卷結果來評估模型的表現。

Compared to major natural language processing tasks, lyric generation is relatively less investigated. The cutting-edge Chinese language model of the time, BERT, or Bidirectional Encoder Representations from Transformers, can successfully encode semantics of words in sentences and by training with large corpus can predict masked words and next sentences with amazingly accuracy. We decide to customize BERT’s ability to the composition of lyrics. By fine-tuning with a large lyric corpus, we wish to use BERT to compose the lyrics by substitution and prediction schemes. Given a lyric template generated by our segmentation algorithm, we show that the model can convert the lyric into another new lyrics by keeping the same length of words in the original lyrics but change its content. We demonstrate the performance of our model and schemes by using both the BLEU metric and subjective human evaluations.

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