  Acronym extraction aims to find acronyms (i.e., short-forms) and their
meanings (i.e., long-forms) from the documents, which is important for
scientific document understanding (SDU@AAAI-22) tasks. Previous works are
devoted to modeling this task as a paragraph-level sequence labeling problem.
However, it lacks the effective use of the external knowledge, especially when
the datasets are in a low-resource setting. Recently, the prompt-based method
with the vast pre-trained language model can significantly enhance the
performance of the low-resourced downstream tasks. In this paper, we propose a
Prompt-based Sequence Generation (PSG) method for the acronym extraction task.
Specifically, we design a template for prompting the extracted acronym texts
with auto-regression. A position extraction algorithm is designed for
extracting the position of the generated answers. The results on the acronym
extraction of Vietnamese and Persian in a low-resource setting show that the
proposed method outperforms all other competitive state-of-the-art (SOTA)
methods.
