A Fuzzy Training Framework for Controllable Sequence-to-Sequence Generation

Abstract

The generation of music lyrics by artificial intelligence (AI) is frequently modeled as a language-targeted sequence-to-sequence generation task. Formally, if we convert the melody into a word sequence, we can consider the lyrics generation task to be a machine translation task. Traditional machine translation tasks involve translating between cross-lingual word sequences, whereas music lyrics generation tasks involve translating between music and natural language word sequences. The theme or key words of the generated lyrics are usually limited to meet the needs of the users when they are generated. This requirement can be thought of as a restricted translation problem. In this paper, we propose a fuzzy training framework that allows a model to simultaneously support both unrestricted and restricted translation by adopting an additional auxiliary training process without constraining the decoding process. This maintains the benefits of restricted translation but greatly reduces the extra time overhead of constrained decoding, thus improving its practicality. The experimental results show that our framework is well suited to the Chinese lyrics generation and restricted machine translation tasks, and that it can also generate language sequence under the condition of given restricted words without training multiple models, thereby achieving the goal of green AI.

Keywords

• Music lyrics generation
• controllable generation
• music understanding
• constrained decoding
• fuzzy training