Legal Transformer Models May Not Always Help
Abstract
Deep learning-based Natural Language Processing methods, especially transformers, have achieved impressive performance in the last few years. Applying those state-of-the-art NLP methods to legal activities to automate or simplify some simple work is of great value. This work investigates the value of domain adaptive pre-training and language adapters in legal NLP tasks. By comparing the performance of language models with domain adaptive pre-training on different tasks and different dataset splits, we show that domain adaptive pre-training is only helpful with low-resource downstream tasks, thus far from being a panacea. We also benchmark the performance of adapters in a typical legal NLP task and show that they can yield similar performance to full model tuning with much smaller training costs. As an additional result, we release LegalRoBERTa, a RoBERTa model further pre-trained on legal corpora.
- Publication:
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arXiv e-prints
- Pub Date:
- September 2021
- DOI:
- 10.48550/arXiv.2109.06862
- arXiv:
- arXiv:2109.06862
- Bibcode:
- 2021arXiv210906862G
- Keywords:
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- Computer Science - Computation and Language