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Domain Adaptive Pretraining for Multilingual Acronym Extraction

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arxiv 2206.15221 v1 pith:52YDBYVI submitted 2022-06-30 cs.CL

Domain Adaptive Pretraining for Multilingual Acronym Extraction

Usama Yaseen , Stefan Langer This is my paper
classification cs.CL
keywords acronymextractionmultilingualtasksharedxlm-robertadomainembeddings
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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