AUTHOR=Church Kenneth , Liu Boxiang 

TITLE=Acronyms and Opportunities for Improving Deep Nets

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=4

YEAR=2021

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2021.732381

DOI=10.3389/frai.2021.732381

ISSN=2624-8212

ABSTRACT=<p>Recently, several studies have reported promising results with BERT-like methods on acronym tasks. In this study, we find an older rule-based program, Ab3P, not only performs better, but error analysis suggests why. There is a well-known spelling convention in acronyms where each letter in the short form (SF) refers to “salient” letters in the long form (LF). The error analysis uses decision trees and logistic regression to show that there is an opportunity for many pre-trained models (BERT, T5, BioBert, BART, ERNIE) to take advantage of this spelling convention.</p>