266 citations · 559 across the 19 of their papers we have counts for
Showing cs.CLShow all
2 papers · 1 filter
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
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…