The interaction of conversational systems with users poses an exciting opportunity for improving them after deployment, but little evidence has been provided of its feasibility. In most applications, users are not able to provide the correct answer to the system, but they are able to provide binary (correct, incorrect) feedback. In this paper we propose feedback-weighted learning based on importance sampling to improve upon an initial supervised system using binary user feedback. We perform simulated experiments on document classification (for development) and Conversational Question Answering datasets like QuAC and DoQA, where binary user feedback is derived from gold annotations. The results show that our method is able to improve over the initial supervised system, getting close to a fully-supervised system that has access to the same labeled examples in in-domain experiments (QuAC), and even matching in out-of-domain experiments (DoQA). Our work opens the prospect to exploit interactions with real users and improve conversational systems after deployment.

本研究提出了一种基于重要性抽样的反馈加权学习方法，利用二进制的用户反馈来改进最初的监督系统。作者在文档分类和对话问答数据集上进行模拟实验，并利用用户提供的二进制反馈来验证方法的有效性和可行性。实验结果表明，反馈加权学习可以显著地提升监督系统的性能，并且具有潜在的应用前景。

使用反馈加权学习改进部署后的对话问答系统