Paper: A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping

ACL ID W09-1107
Title A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2009
Authors

A survey of existing methods for stopping ac- tive learning (AL) reveals the needs for meth- ods that are: more widely applicable; more ag- gressive in saving annotations; and more sta- ble across changing datasets. A new method for stopping AL based on stabilizing predic- tions is presented that addresses these needs. Furthermore, stopping methods are required to handle a broad range of different annota- tion/performance tradeoff valuations. Despite this, the existing body of work is dominated by conservative methods with little (if any) at- tention paid to providing users with control over the behavior of stopping methods. The proposed method is shown to fill a gap in the level of aggressiveness available for stopping AL and supports providing users with control over stopping behavior.