增加RASA的开源自然语言理解管道
Incrementalizing RASA's Open-Source Natural Language Understanding Pipeline
摘要：
随着口语对话系统和聊天机器人的广泛采用，用于自然语言理解的商业和开源服务正在出现。在本文中，我们解释了我们如何改变开源RASA自然语言理解管道以增量方式处理（即，逐词），遵循Schlangen和Skantze提出的增量单元框架。为此，我们更改了现有的RASA组件以增量方式处理，并将更新增量意图识别模型作为组件添加到RASA中。我们对Snips数据集的评估表明，我们的变化允许RASA作为一个有效的增量自然语言理解服务。
英文摘要：
As spoken dialogue systems and chatbots are gaining more widespread adoption, commercial and open-sourced services for natural language understanding are emerging. In this paper, we explain how we altered the open-source RASA natural language understanding pipeline to process incrementally (i.e., word-by-word), following the incremental unit framework proposed by Schlangen and Skantze. To do so, we altered existing RASA components to process incrementally, and added an update-incremental intent recognition model as a component to RASA. Our evaluations on the Snips dataset show that our changes allow RASA to function as an effective incremental natural language understanding service.
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。
