Hierarchical multi-task natural language understanding for cross-domain conversational AI: HERMIT NLU

Andrea Vanzo, Emanuele Bastianelli, Oliver Lemon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a new neural architecture for wide-coverage Natural Language Understanding in Spoken Dialogue Systems. We develop a hierarchical multi-task architecture, which delivers a multi-layer representation of sentence meaning (i.e., Dialogue Acts and Frame-like structures). The architecture is a hierarchy of self-attention mechanisms and BiLSTM encoders followed by CRF tagging layers. We describe a variety of experiments, showing that our approach obtains promising results on a dataset annotated with Dialogue Acts and Frame Semantics. Moreover, we demonstrate its applicability to a different, publicly available NLU dataset annotated with domain-specific intents and corresponding semantic roles, providing overall performance higher than state-of-the-art tools such as RASA, Dialogflow, LUIS, and Watson. For example, we show an average 4.45% improvement in entity tagging F-score over Rasa, Dialogflow and LUIS.

Original languageEnglish
Title of host publicationProceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
PublisherAssociation for Computational Linguistics
Pages254-263
Number of pages10
ISBN (Electronic)9781950737611
DOIs
Publication statusPublished - Sep 2019
Event20th Annual Meeting of the Special Interest Group on Discourse and Dialogue 2019 - Stockholm, Sweden
Duration: 11 Sep 201913 Sep 2019

Conference

Conference20th Annual Meeting of the Special Interest Group on Discourse and Dialogue 2019
Abbreviated titleSIGDIAL 2019
Country/TerritorySweden
CityStockholm
Period11/09/1913/09/19

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Modelling and Simulation

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