An Efficient Active Learning Pipeline for Legal Text Classification

Mamooler, Sepideh; Lebret, Rémi; Massonnet, Stéphane; Aberer, Karl

doi:10.18653/v1/2022.nllp-1.32

Cited by 2 publications

(1 citation statement)

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“…Their experiments show considerable enhancements in data efficiency and stability compared to the standard fine-tuning approach, emphasizing the importance of a suitable training strategy in DAL. Mamooler et al [188] try to combine DAL with PLMs in the legal domain, where they use unlabeled data in three stages: training the model to adjust it to the downstream task, using knowledge distillation to direct the embeddings to a semantically meaningful space, and identifying the initial set.…”

Section: Challenges and Opportunities Of Dalmentioning

confidence: 99%

A Survey on Deep Active Learning: Recent Advances and New Frontiers

Li,

Wang,

Chen

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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Active learning seeks to achieve strong performance with fewer training samples. It does this by iteratively asking an oracle to label newly selected samples in a human-in-the-loop manner. This technique has gained increasing popularity due to its broad applicability, yet its survey papers, especially for deep active learning (DAL), remain scarce. Therefore, we conduct an advanced and comprehensive survey on DAL. We first introduce reviewed paper collection and filtering. Second, we formally define the DAL task and summarize the most influential baselines and widely used datasets. Third, we systematically provide a taxonomy of DAL methods from five perspectives, including annotation types, query strategies, deep model architectures, learning paradigms, and training processes, and objectively analyze their strengths and weaknesses. Then, we comprehensively summarize the main applications of DAL in natural language processing (NLP), computer vision (CV), data mining (DM), and so on. Finally, we discuss challenges and perspectives after a detailed analysis of current studies. This work aims to serve as a useful and quick guide for researchers in overcoming difficulties in DAL. We hope that this survey will spur further progress in this burgeoning field.

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Section: Challenges and Opportunities Of Dalmentioning

confidence: 99%