Scientific documents often contain a large number of acronyms. Disambiguation
of these acronyms will help researchers better understand the meaning of
vocabulary in the documents. In the past, thanks to large amounts of data from
English literature, acronym task was mainly applied in English literature.
However, for other low-resource languages, this task is difficult to obtain
good performance and receives less attention due to the lack of large amount of
annotation data. To address the above issue, this paper proposes an new method
for acronym disambiguation, named as ADBCMM, which can significantly improve
the performance of low-resource languages by building counterfactuals and
multilingual mixing. Specifically, by balancing data bias in low-resource
langauge, ADBCMM will able to improve the test performance outside the data
set. In SDU@AAAI-22 - Shared Task 2: Acronym Disambiguation, the proposed
method won first place in French and Spanish. You can repeat our results here
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本论文提出了一种名为 ADBCMM 的新方法，通过构建对照实验和多语言混合，可以显著提高低资源语言中的简称消歧性能，通过平衡低资源语言中的数据偏差，ADBCMM 能够改善数据集之外的测试性能，在 SDU@AAAI-22 的共享任务 2：简称消歧中，该方法在法语和西班牙语中获得第一名。