42 citations · 93 across the 5 of their papers we have counts for
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cs.CL2020
Contextual Embeddings: When Are They Worth It?
Simran Arora, Avner May, Jian Zhang +1
We study the settings for which deep contextual embeddings (e.g., BERT) give large improvements in performance relative to classic pretrained embeddings (e.g., GloVe), and an even…
cs.CL2020★ 6 cited
Understanding the Downstream Instability of Word Embeddings
Megan Leszczynski, Avner May, Jian Zhang +3
Many industrial machine learning (ML) systems require frequent retraining to keep up-to-date with constantly changing data. This retraining exacerbates a large challenge facing ML…