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cs.CL2020★ 6 cited
Exploring BERT Parameter Efficiency on the Stanford Question Answering Dataset v2.0
Eric Hulburd
In this paper we explore the parameter efficiency of BERT arXiv:1810.04805 on version 2.0 of the Stanford Question Answering dataset (SQuAD2.0). We evaluate the parameter efficienc…
cs.CL2019★ 1 cited
Exploring Neural Net Augmentation to BERT for Question Answering on SQUAD 2.0
Suhas Gupta
Enhancing machine capabilities to answer questions has been a topic of considerable focus in recent years of NLP research. Language models like Embeddings from Language Models (ELM…