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Domain Adaptive Pretraining for Multilingual Acronym Extraction
June 30, 2022 ยท Declared Dead ยท ๐ SDU@AAAI
"No code URL or promise found in abstract"
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Authors
Usama Yaseen, Stefan Langer
arXiv ID
2206.15221
Category
cs.CL: Computation & Language
Citations
5
Venue
SDU@AAAI
Last Checked
5 months ago
Abstract
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.
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