Sethupat Radhakrishna, Arun2019-12-112019-12-112019-08https://hdl.handle.net/11299/208959University of Minnesota M.S. thesis. August 2019. Major: Computer Science. Advisors: Joseph Konstan, Serguei Pakhomov. 1 computer file (PDF); vii, 36 pages.Natural Language Understanding(NLU) is a process of converting the user utterance to a dialog-act after identifying domain, intent and slots from the utterance. User utterances can either contain a single intent or could express multiple intents. Building an NLU module for multi-intent utterances is a huge challenge as traditional state-of-the-art NLU modules do not differentiate between single and multi intent utterances thereby converting them to a single semantic frame which results in reduced performance. In this paper, we introduce a intent based utterance segmenter to split user utterances if each segmented clause corresponds to a different intent. Our experiments evaluate the performance of the utterance segmenter not only on the utterances from movie-ticket booking domain and restaurant reservation domain used for training but also on a new taxi ordering domain. We show that the total number of utterances that are parsed by a utterance segmenter enabled NLU surpass the utterances parsed by traditional NLU.enThesis or Dissertation