ABEX : Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions (2024)

Abstract: We present ABEX, a novel and effective generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks. ABEX is based on ABstract-and-EXpand, a novel paradigm for generating diverse forms of an input document – we first convert a document into its concise, abstract description and then generate new documents based on expanding the resultant abstraction. To learn the task of expanding abstract descriptions, we first train BART on a large-scale synthetic dataset with abstract-document pairs. Next, to generate abstract descriptions for a document, we propose a simple, controllable, and training-free method based on editing AMR graphs. ABEX brings the best of both worlds: by expanding from abstract representations, it preserves the original semantic properties of the documents, like style and meaning, thereby maintaining alignment with the original label and data distribution. At the same time, the fundamental process of elaborating on abstract descriptions facilitates diverse generations. We demonstrate the effectiveness of ABEX on 4 NLU tasks spanning 12 datasets and 4 low-resource settings. ABEX outperforms all our baselines qualitatively with improvements of 0.04% - 38.8%. Qualitatively, ABEX outperforms all prior methods from literature in terms of context and length diversity.

## introduction improving the performance of deep learning models on downstream natural language understanding (nlu) tasks requires sufficient good-quality training data. however, data annotation is an expensive, time-consuming, and noisy task @xcite . data augmentation has proven to be an effective approach for overcoming the data 1 code and data: https://github.com/sreyan88/abex * equal technical contribution. ## methodology overview. fig. 1 illustrates the entire workflow of generating augmentations with abex. the workflow has 2 major steps: (1) we first learn the task of expanding abstract descriptions by fine-tuning bart on a large-scale synthetic dataset. to accomplish this, we first synthesize a dataset d ab , with abstract-document pairs (x ab i ,y ab i ) by prompting llms on a large unlabeled dataset d u . (2) we then generate synthetic augmentations for a downstream nlu dataset d down with documentlabel pairs (x down i ,y down i ) by first converting the documents into abstract descriptions and then employing the fine-tuned bart to generate multiple diverse expansions. directly prompting llms for abstraction and expansion affects controllability, and we also show that it underperforms abex. ## conclusion this paper proposes abex, a novel data augmentation framework based on a novel paradigm -abstract-and-expand. abstract-and-expand involves first abstracting a given document and then expanding it. to achieve this, we fine-tune bart on a large-scale synthetic dataset to learn expanding abstract descriptions and then propose a controllable and training-free method to generate abstract descriptions for downstream dataset documents by editing amr graphs. abex outperforms all our baselines, quantitatively and qualitatively, on various downstream datasets and tasks.