A Case Study of Efficacy and Challenges in Practical Human in Loop Evaluation of NLP Systems Using Checklist
Despite state of the art performance, NLP systems can be fragile in real world situations. This is often due to insufficient understanding of the capabilities and limitations of models and the heavy reliance on standard evaluation benchmarks. Research into non standard evaluation to mitigate this brittleness is gaining increasing attention. Notably, the behavioral testing principle 'Checklist' which decouples testing from implementation revealed significant failures in state of the art models for multiple tasks. In this paper, we present a case study of using Checklist in a practical scenario. We conduct experiments for evaluating an offensive content detection system and use a data augmentation technique for improving the model using insights from Checklist. We lay out the challenges and open questions based on our observations of using Checklist for human in loop evaluation and improvement of NLP systems. Disclaimer: The paper contains examples of content with offensive language. The examples do not represent the views of the authors or their employers towards any person(s) group(s) practice(s) or entity/entities.
HUMEVAL