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

TL;DR

ABEX is a novel data augmentation method for low-resource NLU that generates diverse, semantically consistent documents by expanding abstract descriptions, improving performance across multiple datasets.

Contribution

Introduces ABEX, a new paradigm combining abstract generation and expansion for effective data augmentation in low-resource NLU tasks.

Findings

ABEX outperforms baselines with 0.04%-38.8% improvements.

Generates diverse and semantically aligned data.

Effective across 12 datasets and 4 low-resource settings.

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.