Active Learning for Argument Mining: A Practical Approach
arXiv:2109.13611
Abstract
Despite considerable recent progress, the creation of well-balanced and diverse resources remains a time-consuming and costly challenge in Argument Mining. Active Learning reduces the amount of data necessary for the training of machine learning models by querying the most informative samples for annotation and therefore is a promising method for resource creation. In a large scale comparison of several Active Learning methods, we show that Active Learning considerably decreases the effort necessary to get good deep learning performance on the task of Argument Unit Recognition and Classification (AURC).
References in corpus (13)
- Distributed Representations of Words and Phrases and their Compositionality
- UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
- Improving neural networks by preventing co-adaptation of feature detectors
- On the Convergence of Adam and Beyond
- A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm
- Revisiting Small Batch Training for Deep Neural Networks
- A Benchmark and Comparison of Active Learning for Logistic Regression
- Diverse mini-batch Active Learning
- Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning
- Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks
- Discovering General-Purpose Active Learning Strategies
- Sequence Labeling: A Practical Approach
- Investigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text Datasets