Create! Don’t Repeat: A Paradigm Shift in Multi-Label Augmentation through Label Creative Generation (2024)

Abstract: We propose Label Creative Generation (LCG), a new paradigm in multi-label data augmentation. Beyond repeating data points with fixed labels, LCG creates new data by exploring innovative label combinations. Within LCG, we introduce Tail-Driven Conditional Augmentation (TDCA), combining tail-driven label sampling and label-conditioned text generation for balanced, consistent data augmentation. Our approach has demonstrated a **100.21%** increase in PSP@1 across three datasets, successfully mitigating the long-tail effect in MLTC and markedly enhancing model performance.

## introduction multi-label text classification (mltc), prevalent in fields such as sentiment analysis and recommendation systems, grapples with the dual challenges of an extensive label space and a pronounced longtail distribution. this imbalance is exemplified in wiki10-31k dataset, where a mere 1.5% of labels have more than 100 training instances, leaving the vast majority with a scarcity of training data. ## tail-driven conditional augmentation multi-label text classification (mltc) aims to assign a subset of labels y ⊆ l to each instance x, where @xmath0, l 2 , ..., l n } represents the entire possible label space. this task can be formalized as learning a mapping function f (x) → 2 l , predicting the power set of l. the approach of tdca is straightforward: it commences with tail-driven label sampling to create label combinations and proceeds to contrastive label-conditioned generation, synthesizing text that aligns with these labels. ## conclusion in this paper, we introduce a new multi-label data augmentation paradigm named label creative generation (lcg). under lcg, we propose tail-driven conditional augmentation (tdca). tdca facilitates the creation of balanced label combinations by dual-weighted label graph and tail-driven label sampling. furthermore, tdca employs a contrastive label-conditioned generation to produce augmented texts that match each label and maintain consistency with the original dataset. empirical evaluations on three datasets with varying label counts demonstrate the effectiveness of tdca. our approach significantly surpasses existing data augmentation methods, effectively mitigates the long-tail effect, and enhances model prediction performance in mltc.