Incrementalizing RASA's Open-Source Natural Language Understanding Pipeline

Abstract

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.