Papers with relative error
Syntactic Structure Distillation Pretraining for Bidirectional Encoders (2020.tacl-1)
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Adhiguna Kuncoro, Lingpeng Kong, Daniel Fried, Dani Yogatama, Laura Rimell, Chris Dyer, Phil Blunsom
| Challenge: | Textual representation learners trained on large amounts of data have been successful on downstream tasks. |
| Approach: | They propose a knowledge distillation strategy for injecting syntactic biases into BERT pretraining by distilling the approximate marginal distribution over words in context from the syntaktic LM. |
| Outcome: | The proposed method reduces relative error by 2–21% on a diverse set of structured prediction tasks. |
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. |