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.
Cite
@article{arxiv.2206.15221,
title = {Domain Adaptive Pretraining for Multilingual Acronym Extraction},
author = {Usama Yaseen and Stefan Langer},
journal= {arXiv preprint arXiv:2206.15221},
year = {2022}
}