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| MNLI | QNLI | QQP | SST-2 | CoLA | MRPC | RTE | STS-B | Average | |
|---|---|---|---|---|---|---|---|---|---|
| BERT(HF) | 84.12 | 90.69 | 90.75 | 92.52 | 58.89 | 86.17 | 68.67 | 89.39 | 82.65 |
| DictBERT | 84.36 | 91.02 | 90.78 | 92.43 | 61.81 | 87.25 | 72.90 | 89.40 | 83.74 |
| MNLI | QNLI | QQP | SST-2 | CoLA | MRPC | RTE | STS-B | Average | |
|---|---|---|---|---|---|---|---|---|---|
| w/o dict | 84.24 | 90.99 | 90.80 | 92.51 | 60.50 | 87.04 | 73.75 | 89.37 | 83.69 |
1@inproceedings{yu2022dict,
2 title={Dict-BERT: Enhancing Language Model Pre-training with Dictionary},
3 author={Yu, Wenhao and Zhu, Chenguang and Fang, Yuwei and Yu, Donghan and Wang, Shuohang and Xu, Yichong and Zeng, Michael and Jiang, Meng},
4 booktitle={Findings of the Association for Computational Linguistics: ACL 2022},
5 pages={1907--1918},
6 year={2022}
7}