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A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition

Yuxuan Chen; Jonas Mikkelsen; Arne Björn Binder; Christoph Alt; Leonhard Hennig
In: Proceedings of the 7th Workshop on Representation Learning for NLP (ACL-REPL4NLP 2022). ACL Workshop on Representation Learning for NLP (RepL4NLP-2022), located at ACL 2022, May 22-27, Dublin, Ireland, ACL, 5/2022.


Pre-trained language models (PLM) are effective components of few-shot named entity recognition (NER) approaches when augmented with continued pre-training on task-specific out-of-domain data or fine-tuning on in-domain data. However, their performance in low-resource scenarios, where such data is not available, remains an open question. We introduce an encoder evaluation framework, and use it to systematically compare the performance of state-of-the-art pre-trained representations on the task of low-resource NER. We analyze a wide range of encoders pre-trained with different strategies, model architectures, intermediate-task fine-tuning, and contrastive learning. Our experimental results across ten benchmark NER datasets in English and German show that encoder performance varies significantly, suggesting that the choice of encoder for a specific low-resource scenario needs to be carefully evaluated.


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