In a controllable text generation dataset, there exist unannotated attributes that could provide irrelevant learning signals to models that use it for training and thus degrade their performance. We propose focused prefix tuning(FPT) to mitigate the problem and to enable the control to focus on the desired attribute. Experimental results show that FPT can achieve better control accuracy and text fluency than baseline models in single-attribute control tasks. In multi-attribute control tasks, FPT achieves comparable control accuracy with the state-of-the-art approach while keeping the flexibility to control new attributes without retraining existing models.

翻译：在可控文本生成数据集中，存在未标注的属性，这些属性可能为使用该数据集训练的模型提供无关的学习信号，从而降低模型性能。我们提出聚焦前缀微调（FPT）方法来缓解这一问题，并使控制聚焦于目标属性。实验结果表明，在单属性控制任务中，FPT能够实现比基线模型更好的控制准确率和文本流畅度。在多属性控制任务中，FPT在保持无需重新训练现有模型即可控制新属性的灵活性的同时，实现了与最先进方法相当的控制准确率。