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@@ -22,15 +22,15 @@ Please see the [casehold repository](https://github.com/reglab/casehold) for scr
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  See `demo.ipynb` in the casehold repository for details on calculating domain specificity (DS) scores for tasks or task examples by taking the difference in pretrain loss on BERT (double) and Legal-BERT. DS score may be readily extended to estimate domain specificity of tasks in other domains using BERT (double) and existing pretrained models (e.g., [SciBERT](https://arxiv.org/abs/1903.10676)).
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  ### Citation
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- \t@inproceedings{zhengguha2021,
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- \t\ttitle={When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset},
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- \t\tauthor={Lucia Zheng and Neel Guha and Brandon R. Anderson and Peter Henderson and Daniel E. Ho},
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- \t\tyear={2021},
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- \t\teprint={2104.08671},
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- \t\tarchivePrefix={arXiv},
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- \t\tprimaryClass={cs.CL},
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- \t\tbooktitle={Proceedings of the 18th International Conference on Artificial Intelligence and Law},
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- \t\tpublisher={Association for Computing Machinery}
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- \t}
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  Lucia Zheng, Neel Guha, Brandon R. Anderson, Peter Henderson, and Daniel E. Ho. 2021. When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset. In *Proceedings of the 18th International Conference on Artificial Intelligence and Law (ICAIL '21)*, June 21-25, 2021, São Paulo, Brazil. ACM Inc., New York, NY, (in press). arXiv: [2104.08671 [cs.CL]](https://arxiv.org/abs/2104.08671).
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  See `demo.ipynb` in the casehold repository for details on calculating domain specificity (DS) scores for tasks or task examples by taking the difference in pretrain loss on BERT (double) and Legal-BERT. DS score may be readily extended to estimate domain specificity of tasks in other domains using BERT (double) and existing pretrained models (e.g., [SciBERT](https://arxiv.org/abs/1903.10676)).
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  ### Citation
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+ @inproceedings{zhengguha2021,
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+ title={When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset},
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+ author={Lucia Zheng and Neel Guha and Brandon R. Anderson and Peter Henderson and Daniel E. Ho},
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+ year={2021},
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+ eprint={2104.08671},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL},
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+ booktitle={Proceedings of the 18th International Conference on Artificial Intelligence and Law},
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+ publisher={Association for Computing Machinery}
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+ }
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  Lucia Zheng, Neel Guha, Brandon R. Anderson, Peter Henderson, and Daniel E. Ho. 2021. When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset. In *Proceedings of the 18th International Conference on Artificial Intelligence and Law (ICAIL '21)*, June 21-25, 2021, São Paulo, Brazil. ACM Inc., New York, NY, (in press). arXiv: [2104.08671 [cs.CL]](https://arxiv.org/abs/2104.08671).