We describe and validate a metric for estimating multi-class classifier
performance based on cross-validation and adapted for improvement of small,
unbalanced natural-language datasets used in chatbot design. Our experiences
draw upon building recruitment chatbots that mediate communication between
job-seekers and recruiters by exposing the ML/NLP dataset to the recruiting
team. Evaluation approaches must be understandable to various stakeholders, and
useful for improving chatbot performance. The metric, nex-cv, uses negative
examples in the evaluation of text classification, and fulfils three
requirements. First, it is actionable: it can be used by non-developer staff.
Second, it is not overly optimistic compared to human ratings, making it a fast
method for comparing classifiers. Third, it allows model-agnostic comparison,
making it useful for comparing systems despite implementation differences. We
validate the metric based on seven recruitment-domain datasets in English and
German over the course of one year.

我们描述并验证了一种基于交叉验证的度量标准，用于评估小型、不平衡的自然语言数据集在聊天机器人设计中的应用，该度量标准利用负样例在文本分类的评估中，并满足可行性、可比较和模型不可知等三个要求。