  Despite significant advancements in multi-label text classification, the
ability of existing models to generalize to novel and seldom-encountered
complex concepts, which are compositions of elementary ones, remains
underexplored. This research addresses this gap. By creating unique data splits
across three benchmarks, we assess the compositional generalization ability of
existing multi-label text classification models. Our results show that these
models often fail to generalize to compositional concepts encountered
infrequently during training, leading to inferior performance on tests with
these new combinations. To address this, we introduce a data augmentation
method that leverages two innovative text generation models designed to enhance
the classification models' capacity for compositional generalization. Our
experiments show that this data augmentation approach significantly improves
the compositional generalization capabilities of classification models on our
benchmarks, with both generation models surpassing other text generation
baselines.
