{
  "id": "1907.05403",
  "version": "v1",
  "published": "2019-07-11T17:35:20.000Z",
  "updated": "2019-07-11T17:35:20.000Z",
  "title": "Incrementalizing RASA's Open-Source Natural Language Understanding Pipeline",
  "authors": [
    "Andrew Rafla",
    "Casey Kennington"
  ],
  "comment": "5 pages, 1 figure",
  "categories": [
    "cs.CL"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-07-11T17:35:20.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "open-source natural language understanding pipeline",
      "incrementalizing rasas open-source natural language",
      "rasas open-source natural language understanding",
      "natural language understanding service"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 5,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}