How to talk to strangers: Generating medical reports for first-Time users

Dimitra Gkatzia, Verena Rieser, Oliver Lemon

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

1 Citation (Scopus)

Abstract

We propose a novel approach for handling first-Time users in the context of automatic report generation from timeseries data in the health domain. Handling first-Time users is a common problem for Natural Language Generation (NLG) and interactive systems in general -The system cannot adapt to users without prior interaction or user knowledge. In this paper, we propose a novel framework for generating medical reports for first-Time users, using multi-objective optimisation (MOO) to account for the preferences of multiple possible user types, where the content preferences of potential users are modelled as objective functions. Our proposed approach outperforms two meaningful baselines in an evaluation with prospective users, yielding large (= .79) and medium (= .46) effect sizes respectively.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
PublisherIEEE
Pages579-586
Number of pages8
ISBN (Electronic)9781509006250
DOIs
Publication statusPublished - 10 Nov 2016
Event2016 IEEE International Conference on Fuzzy Systems - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Conference

Conference2016 IEEE International Conference on Fuzzy Systems
Abbreviated titleFUZZ-IEEE 2016
CountryCanada
CityVancouver
Period24/07/1629/07/16

ASJC Scopus subject areas

  • Control and Optimization
  • Logic
  • Modelling and Simulation

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