Ranger: A Toolkit for Effect-Size Based Multi-Task Evaluation
This toolkit addresses the problem of unreliable conclusions in multi-task evaluation for NLP and IR researchers, though it is incremental as it applies existing statistical methods to these domains.
The paper tackles the challenge of aggregating results over incomparable metrics and scenarios in NLP and IR by introducing Ranger, a toolkit for effect-size-based meta-analysis, which produces publication-ready forest plots to facilitate robust multi-task evaluation.
In this paper, we introduce Ranger - a toolkit to facilitate the easy use of effect-size-based meta-analysis for multi-task evaluation in NLP and IR. We observed that our communities often face the challenge of aggregating results over incomparable metrics and scenarios, which makes conclusions and take-away messages less reliable. With Ranger, we aim to address this issue by providing a task-agnostic toolkit that combines the effect of a treatment on multiple tasks into one statistical evaluation, allowing for comparison of metrics and computation of an overall summary effect. Our toolkit produces publication-ready forest plots that enable clear communication of evaluation results over multiple tasks. Our goal with the ready-to-use Ranger toolkit is to promote robust, effect-size-based evaluation and improve evaluation standards in the community. We provide two case studies for common IR and NLP settings to highlight Ranger's benefits.