Efficient Annotator Reliablity Assessment with EffiARA

Data annotation is an essential component of the machine learning pipeline; it is also a costly and time-consuming process. With the introduction of transformer-based models, annotation at the document level is increasingly popular; however, there is no standard framework for structuring such tasks. The EffiARA annotation framework is, to our knowledge, the first project to support the whole annotation pipeline, from understanding the resources required for an annotation task to compiling the annotated dataset and gaining insights into the reliability of individual annotators as well as the dataset as a whole. The framework's efficacy is supported by two previous studies: one improving classification performance through annotator-reliability-based soft label aggregation and sample weighting, and the other increasing the overall agreement among annotators through removing identifying and replacing an unreliable annotator. This work introduces the EffiARA Python package and its accompanying webtool, which provides an accessible graphical user interface for the system. We open-source the EffiARA Python package atthis https URLand the webtool is publicly accessible atthis https URL.

@article{cook2025_2504.00589,
  title={ Efficient Annotator Reliablity Assessment with EffiARA },
  author={ Owen Cook and Jake Vasilakes and Ian Roberts and Xingyi Song },
  journal={arXiv preprint arXiv:2504.00589},
  year={ 2025 }
}