论文标题
从大规模领域特定知识基础上建立聊天机器人：挑战和机遇
Building chatbots from large scale domain-specific knowledge bases: challenges and opportunities
论文作者
论文摘要
流行的对话代理框架，例如Alexa技能套件（ASK）和Google Actions（Gactions），为促进各种垂直领域的语音AI解决方案的开发和部署提供了前所未有的机会。然而，使用这些框架以高精度了解用户话仍然是一项艰巨的任务。特别是，当构建具有大量域特异性实体的聊天机器人时。在本文中，我们描述了从建立大规模虚拟助手来理解和回应与设备相关的投诉的大规模虚拟助手中学到的挑战和经验教训。在此过程中，我们描述了一个替代性可扩展框架：1）从短文本中提取有关设备组件及其相关问题实体的知识，以及2）学习在用户话语中识别此类实体。我们通过在真实数据集上的评估中表明，与现成的流行者相比，提议的框架更加缩小，大量实体的准确性高达30％，并且更有效地了解具有特定于域特异性实体的用户话语。
Popular conversational agents frameworks such as Alexa Skills Kit (ASK) and Google Actions (gActions) offer unprecedented opportunities for facilitating the development and deployment of voice-enabled AI solutions in various verticals. Nevertheless, understanding user utterances with high accuracy remains a challenging task with these frameworks. Particularly, when building chatbots with large volume of domain-specific entities. In this paper, we describe the challenges and lessons learned from building a large scale virtual assistant for understanding and responding to equipment-related complaints. In the process, we describe an alternative scalable framework for: 1) extracting the knowledge about equipment components and their associated problem entities from short texts, and 2) learning to identify such entities in user utterances. We show through evaluation on a real dataset that the proposed framework, compared to off-the-shelf popular ones, scales better with large volume of entities being up to 30% more accurate, and is more effective in understanding user utterances with domain-specific entities.
