Papers with one-time representation

1 papers
BERT Has More to Offer: BERT Layers Combination Yields Better Sentence Embeddings (2023.findings-emnlp)

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Challenge: Obtaining sentence representations from BERT-based models is valuable as it takes less time to pre-compute a one-time representation of the data and then use it for the downstream tasks.
Approach: They propose to combine certain layers of a BERT-based model rested on the data set and model to achieve substantially better results.
Outcome: The proposed method outperforms baseline models on seven semantic textual similarity datasets and on eight transfer data sets.

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