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Status: Unchecked ×Keyword: zero-shot cross-lingual transfer ×Clear all

2 claims from 1 paper

1. Computer Science › Topic Modeling

   DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing

   He, Gao and Chen · arXiv (Cornell University) · 2021

   The paper presents DeBERTaV3, which swaps masked language modelling for replaced token detection and adds a new embedding-sharing method, reporting state-of-the-art results on language understanding benchmarks.

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   1. UncheckedThe paper says that plain embedding sharing in ELECTRA-style pre-training makes training less efficient and lowers the resulting model's performance.“Our analysis shows that vanilla embedding sharing in ELECTRA hurts training efficiency and model performance.”
   2. UncheckedDeBERTaV3 Large scores an average 91.37% on the eight GLUE tasks, 1.37% above DeBERTa and 1.91% above ELECTRA, a new best among similar-structure models.“Taking the GLUE benchmark with eight tasks as an example, the DeBERTaV3 Large model achieves a 91.37% average score, which is 1.37% over DeBERTa and 1.91% over ELECTRA, setting a new state-of-the-art (SOTA) among the models with a similar structure.”