论文标题
会话文件预测以协助客户服务代理商
Conversational Document Prediction to Assist Customer Care Agents
论文作者
论文摘要
客户服务对话中经常发生的模式是代理商使用适当的网页URL响应，以满足用户需求。我们研究了预测客户服务代理可以用来促进用户需求的文档的任务。我们还引入了一个新的公共数据集，该数据集支持上述问题。使用此数据集和其他两个数据集，我们研究了任务的最先进的深度学习（DL）和信息检索模型。此外，我们在推理时间复杂性方面分析了此类系统的实用性。我们的节目表明，混合IR+DL方法提供了两全其美的最好。
A frequent pattern in customer care conversations is the agents responding with appropriate webpage URLs that address users' needs. We study the task of predicting the documents that customer care agents can use to facilitate users' needs. We also introduce a new public dataset which supports the aforementioned problem. Using this dataset and two others, we investigate state-of-the art deep learning (DL) and information retrieval (IR) models for the task. Additionally, we analyze the practicality of such systems in terms of inference time complexity. Our show that an hybrid IR+DL approach provides the best of both worlds.
