We propose a machine-translation approach to automatically generate a
playlist title from a set of music tracks. We take a sequence of track IDs as
input and a sequence of words in a playlist title as output, adapting the
sequence-to-sequence framework based on Recurrent Neural Network (RNN) and
Transformer to the music data. Considering the orderless nature of music tracks
in a playlist, we propose two techniques that remove the order of the input
sequence. One is data augmentation by shuffling and the other is deleting the
positional encoding. We also reorganize the existing music playlist datasets to
generate phrase-level playlist titles. The result shows that the Transformer
models generally outperform the RNN model. Also, removing the order of input
sequence improves the performance further.

本文提出一种机器翻译方法，从一组音乐曲目中自动生成播放列表标题。作者采用序列到序列的框架，基于递归神经网络和 Transformer 来对音乐数据进行处理。考虑到播放列表中歌曲的无序性，作者提出两种方法来消除输入序列的顺序，即打乱和删除位置编码。结果显示，Transformer 模型通常优于 RNN 模型。此外，消除输入序列的顺序进一步提高了性能。