  This paper presents our findings from participating in the multilingual
acronym extraction shared task SDU@AAAI-22. The task consists of acronym
extraction from documents in 6 languages within scientific and legal domains.
To address multilingual acronym extraction we employed BiLSTM-CRF with
multilingual XLM-RoBERTa embeddings. We pretrained the XLM-RoBERTa model on the
shared task corpus to further adapt XLM-RoBERTa embeddings to the shared task
domain(s). Our system (team: SMR-NLP) achieved competitive performance for
acronym extraction across all the languages.
