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hi my name is Simran and Iamp;#39;m a PhD student at Stanford University in Chris Rayamp;#39;s group today Iamp;#39;m excited to talk to you about a study to better understand the value of contextual headings the recent development of rich conductive word embeddings such as Alvin Burt has revolutionized NLP enabling rapid progress and popular benchmarks like glue and saying widespread industrial use these embeddings are trained to model the context in which a word appears in a sentence and although contextual embeddings perform incredibly well theyamp;#39;re highly computationally expensive at both training and inference time as they generally consist of several layers of transformer modules for example when using the verb base model extracting the word embeddings for the s in a sentence takes on the order of 10 milliseconds on a GPU and requires storing hundreds of megabytes of model parameters and gigabytes of model activations if the embeddings are being fine-tuned in this work
