Self-training improves few-shot learning in legal artificial intelligence tasks

Author:

Zhou YulinORCID,Qin Yongbin,Huang Ruizhang,Chen Yanping,Lin Chuan,Zhou Yuan

Funder

National Natural Science Foundation of China

Key Technology R &D Program of Guizhou Province

National Key R &D Program of China

Publisher

Springer Science and Business Media LLC

Reference57 articles.

1. Arora S, Liang Y, Ma T (2017) A simple but tough-to-beat baseline for sentence embeddings. In: international conference on learning representations, pp 1–16

2. Bao Y, Wu M, Chang S, Barzilay R (2019) Few-shot text classification with distributional signatures. arXiv preprint arXiv:1908.06039

3. Bhattacharya P, Paul S, Ghosh K, Ghosh S, Wyner A (2023) Deeprhole: deep learning for rhetorical role labeling of sentences in legal case documents. Artif Intell Law 31(1):53–90

4. Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A et al (2020) Language models are few-shot learners. Adv Neural Inform Process Syst 33:1877–1901

5. Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A et al (2020) Language models are few-shot learners. Adv Neural Inform Process Syst 33:1877–1901

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