  How to automatically synthesize natural-looking dance movements based on a
piece of music is an incrementally popular yet challenging task. Most existing
data-driven approaches require hard-to-get paired training data and fail to
generate long sequences of motion due to error accumulation of autoregressive
structure. We present a novel 3D dance synthesis system that only needs
unpaired data for training and could generate realistic long-term motions at
the same time. For the unpaired data training, we explore the disentanglement
of beat and style, and propose a Transformer-based model free of reliance upon
paired data. For the synthesis of long-term motions, we devise a new
long-history attention strategy. It first queries the long-history embedding
through an attention computation and then explicitly fuses this embedding into
the generation pipeline via multimodal adaptation gate (MAG). Objective and
subjective evaluations show that our results are comparable to strong baseline
methods, despite not requiring paired training data, and are robust when
inferring long-term music. To our best knowledge, we are the first to achieve
unpaired data training - an ability that enables to alleviate data limitations
effectively. Our code is released on https://github.com/BFeng14/RobustDancer
