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RoBERTa: A Robustly Optimized BERT Pretraining Approach

2019), and XLNet (Yang et al., 2019) have brought significant performance gains, but it can be challenging to determine which aspects of the methods contribute the most. Training is computationally expensive, limiting the amount of tuning that can be done, and is often done with private training data of varying sizes, limiting

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Sentence-BERT: Sentence Embeddings using Siamese BERT …

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also tested XLNet (Yang et al.,2019), but it led in general to worse results than BERT. A large disadvantage of the BERT network structure is that no independent sentence embed-dings are computed, which makes it difficult to de-rive sentence embeddings from BERT. To bypass this limitations, researchers passed single sen-

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