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Status: Unchecked ×Keyword: sentence encoders ×Clear all

2 claims from 1 paper

1. Computer Science › Topic Modeling

   Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks

   Phang, Févry and Bowman · arXiv (Cornell University) · 2018

   The paper adds supplementary training on labelled tasks to pretrained sentence encoders, reporting a GLUE score of 81.8 with BERT and gains for ELMo and another model, especially with little data.

   Unchecked2 claims
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   1. UncheckedAdding training on a large labelled task such as natural language inference, after language-model pretraining, improved GLUE benchmark results.“By supplementing language model-style pretraining with further training on data-rich supervised tasks, such as natural language inference, we obtain additional performance improvements on the GLUE benchmark.”
   2. UncheckedExtra training of BERT on a data-rich labelled task reached a GLUE score of 81.8, 1.4 points above BERT, the state of the art as of 24 February 2019.“Applying supplementary training on BERT (Devlin et al., 2018), we attain a GLUE score of 81.8---the state of the art (as of 02/24/2019) and a 1.4 point improvement over BERT.”