Domain Adaptive Pretraining for Multilingual Acronym Extraction
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Abstract (arXiv)
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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References · 6
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
- 2016Neural Architectures for Named Entity Recognitioncited by 550
- 2014Growth rates of modern science: A bibliometric analysis based on the number of publications and cited referencescited by 64
- 2020What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguationcited by 26
- 2022MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extractioncited by 4
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