  Acronym disambiguation (AD) task aims to find the correct expansions of an
ambiguous ancronym in a given sentence. Although it is convenient to use
acronyms, sometimes they could be difficult to understand. Identifying the
appropriate expansions of an acronym is a practical task in natural language
processing. Since few works have been done for AD in scientific field, we
propose a binary classification model incorporating BERT and several training
strategies including dynamic negative sample selection, task adaptive
pretraining, adversarial training and pseudo labeling in this paper.
Experiments on SciAD show the effectiveness of our proposed model and our score
ranks 1st in SDU@AAAI-21 shared task 2: Acronym Disambiguation.
