Compositional generalization, representing the model's ability to generate text with new attribute combinations obtained by recombining single attributes from the training data, is a crucial property for multi-aspect controllable text generation (MCTG) methods. Nonetheless, a comprehensive compositional generalization evaluation benchmark of MCTG is still lacking. We propose CompMCTG, a benchmark encompassing diverse multi-aspect labeled datasets and a crafted three-dimensional evaluation protocol, to holistically evaluate the compositional generalization of MCTG approaches. We observe that existing MCTG works generally confront a noticeable performance drop in compositional testing. To mitigate this issue, we introduce Meta-MCTG, a training framework incorporating meta-learning, where we enable models to learn how to generalize by simulating compositional generalization scenarios in the training phase. We demonstrate the effectiveness of Meta-MCTG through achieving obvious improvement (by at most 3.64%) for compositional testing performance in 94.4% cases.

翻译：组合泛化能力，即模型通过重组训练数据中的单一属性来生成具有新属性组合文本的能力，是多维度可控文本生成（MCTG）方法的关键特性。然而，目前仍缺乏针对MCTG的综合性组合泛化评估基准。我们提出了CompMCTG基准，该基准包含多样化的多维度标注数据集和精心设计的三维评估方案，以全面评估MCTG方法的组合泛化能力。我们观察到，现有MCTG方法在组合测试中普遍面临显著的性能下降。为缓解此问题，我们提出了Meta-MCTG训练框架，该框架融入元学习思想，通过在训练阶段模拟组合泛化场景，使模型学习如何实现泛化。实验证明，Meta-MCTG在94.4%的案例中显著提升了组合测试性能（最高提升达3.64%），验证了其有效性。