有些人不值得倾听：定期用最终用户团队的反馈重新训练分类器
Some people aren't worth listening to: periodically retraining classifiers with feedback from a team of end users
摘要：
文档分类是无处不在的商业环境中，但往往是一个分类器的最终用户从事一个持续的反馈-再训练循环的团队，维护it. We认为这种反馈-再训练循环从多代理的角度来看，考虑最终用户作为自主智能体，提供分类器提供的标记数据的反馈。这使我们能够检查不可靠的最终用户提供不正确的反馈对分类器性能的影响。我们展示了一个分类器，它可以学习哪些用户倾向于不可靠，将他们的反馈过滤出循环，从而提高后续迭代的性能。
英文摘要：
Document classification is ubiquitous in a business setting, but often the end users of a classifier are engaged in an ongoing feedback-retrain loop with the team that maintain it. We consider this feedback-retrain loop from a multi-agent point of view, considering the end users as autonomous agents that provide feedback on the labelled data provided by the classifier. This allows us to examine the effect on the classifier's performance of unreliable end users who provide incorrect feedback. We demonstrate a classifier that can learn which users tend to be unreliable, filtering their feedback out of the loop, thus improving performance in subsequent iterations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
