Fetching the paper…
Reading the bibliography…
Deep learning-based Natural Language Processing methods, especially transformers, have achieved impressive performance in the last few years.
To pretrain or not to pretrain: Examining the benefits of pretraining on resource rich tasks
Wang, S., Khabsa, M., and Ma, H. (2020) · 2006
Earlier work this paper cites.
Lexa: Towards automatic legal citation classification
Galgani, F. and Hoffmann, A. (2010) · 2010
Earlier work this paper cites.
UCI machine learning repository
Dua, D. and Graff, C. (2017) · 2017
Earlier work this paper cites.
Vse++: Improving visual-semantic embeddings with hard negatives
Faghri, F., Fleet, D. J., Kiros, J. R., and Fidler, S. (2018) · 2018
Earlier work this paper cites.
Large-scale multi-label text classification on EU legislation
Chalkidis, I., Fergadiotis, E., Malakasiotis, P., and Androutsopoulos, I. (2019) · 2019
Earlier work this paper cites.
Law and word order: Nlp in legal tech
DALE, R. (2019) · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019) · 2019
Cited alongside, same era.
Parameter-efficient transfer learning for nlp
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., de Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S. (2019) · 2019
Cited alongside, same era.
Biobert: a pre-trained biomedical language representation model for biomedical text mining
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., and Kang, J. (2019) · 2019
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019) · 2019
Cited alongside, same era.
Legal-bert: The muppets straight out of law school
Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Aletras, N., and Androutsopoulos, I. (2020) · 2020
Later among the works it cites.
Don’t stop pretraining: Adapt language models to domains and tasks
Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., and Smith, N. A. (2020) · 2020
Later among the works it cites.
On the importance of pre-training data volume for compact language models
Micheli, V., d’Hoffschmidt, M., and Fleuret, F. (2020) · 2020
Later among the works it cites.
Neural contract element extraction revisited: Letters from sesame street
Chalkidis, I., Fergadiotis, M., Malakasiotis, P., and Androutsopoulos, I. (2021) · 2021
Closest in time.
When does pretraining help? assessing self-supervised learning for law and the casehold dataset
Zheng, L., Guha, N., Anderson, B. R., Henderson, P., and Ho, D. E. (2021) · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…