This document presents some early explorations of applying Softly Masked Language Modelling (SMLM) to symbolic music generation. SMLM can be seen as a generalisation of masked language modelling (MLM), where instead of each element of the input set being either known or unknown, each element can be known, unknown or partly known. We demonstrate some results of applying SMLM to constrained symbolic music generation using a transformer encoder architecture. Several audio examples are available at https://erl-j.github.io/smlm-web-supplement/

翻译：本文展示了将软掩码语言建模（SMLM）应用于符号音乐生成的初步探索。SMLM可被视为掩码语言建模（MLM）的泛化形式，其中输入集合中的每个元素不再仅限于已知或未知两种状态，而是可以处于已知、未知或部分已知三种状态。我们展示了采用Transformer编码器架构将SMLM应用于受限符号音乐生成的部分结果。相关音频示例可访问 https://erl-j.github.io/smlm-web-supplement/ 获取。