  An obstacle to scientific document understanding is the extensive use of
acronyms which are shortened forms of long technical phrases. Acronym
disambiguation aims to find the correct meaning of an ambiguous acronym in a
given text. Recent efforts attempted to incorporate word embeddings and deep
learning architectures, and achieved significant effects in this task. In
general domains, kinds of fine-grained pretrained language models have sprung
up, thanks to the largescale corpora which can usually be obtained through
crowdsourcing. However, these models based on domain agnostic knowledge might
achieve insufficient performance when directly applied to the scientific
domain. Moreover, obtaining large-scale high-quality annotated data and
representing high-level semantics in the scientific domain is challenging and
expensive. In this paper, we consider both the domain agnostic and specific
knowledge, and propose a Hierarchical Dual-path BERT method coined hdBERT to
capture the general fine-grained and high-level specific representations for
acronym disambiguation. First, the context-based pretrained models, RoBERTa and
SciBERT, are elaborately involved in encoding these two kinds of knowledge
respectively. Second, multiple layer perceptron is devised to integrate the
dualpath representations simultaneously and outputs the prediction. With a
widely adopted SciAD dataset contained 62,441 sentences, we investigate the
effectiveness of hdBERT. The experimental results exhibit that the proposed
approach outperforms state-of-the-art methods among various evaluation metrics.
Specifically, its macro F1 achieves 93.73%.
