Abstract
This paper presents a novel, syllable-structured Chinese lyrics generation model given a piece of original melody. Most previously reported lyrics generation models fail to include the relationship between lyrics and melody. In this work, we propose to interpret lyrics-melody alignments as syllable structural information and use a multi-channel sequence-to-sequence model with considering both phrasal structures and semantics. Two different RNN encoders are applied, one of which is for encoding syllable structures while the other for semantic encoding with contextual sentences or input keywords. Moreover, a large Chinese lyrics corpus for model training is leveraged. With automatic and human evaluations, results demonstrate the effectiveness of our proposed lyrics generation model. To the best of our knowledge, there is few previous reports on lyrics generation considering both music and linguistic perspectives.
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Acknowledgement
This work was supported by Ping An Technology (Shenzhen) Co., Ltd., China.
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Lu, X., Wang, J., Zhuang, B., Wang, S., Xiao, J. (2019). A Syllable-Structured, Contextually-Based Conditionally Generation of Chinese Lyrics. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11672. Springer, Cham. https://doi.org/10.1007/978-3-030-29894-4_20
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DOI: https://doi.org/10.1007/978-3-030-29894-4_20
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