  Acronyms are abbreviated units of a phrase constructed by using initial
components of the phrase in a text. Automatic extraction of acronyms from a
text can help various Natural Language Processing tasks like machine
translation, information retrieval, and text summarisation. This paper
discusses an ensemble approach for the task of Acronym Extraction, which
utilises two different methods to extract acronyms and their corresponding long
forms. The first method utilises a multilingual contextual language model and
fine-tunes the model to perform the task. The second method relies on a
convolutional neural network architecture to extract acronyms and append them
to the output of the previous method. We also augment the official training
dataset with additional training samples extracted from several open-access
journals to help improve the task performance. Our dataset analysis also
highlights the noise within the current task dataset. Our approach achieves the
following macro-F1 scores on test data released with the task: Danish (0.74),
English-Legal (0.72), English-Scientific (0.73), French (0.63), Persian (0.57),
Spanish (0.65), Vietnamese (0.65). We release our code and models publicly.
