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How to Talk to Strangers: generating medical reports for first time users

Gkatzia, Dimitra; Rieser, Verena; Lemon, Oliver

Authors

Verena Rieser

Oliver Lemon



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.

Presentation Conference Type Conference Paper (Published)
Conference Name FUZZ-IEEE 2016
Start Date Jul 24, 2016
End Date Jul 26, 2016
Acceptance Date Mar 14, 2016
Online Publication Date Nov 10, 2016
Publication Date Nov 10, 2016
Deposit Date Mar 16, 2016
Publicly Available Date Nov 10, 2016
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Book Title 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
ISBN 978-1-5090-0626-7, 978-1-5090-0625-0,
DOI https://doi.org/10.1109/FUZZ-IEEE.2016.7737739
Keywords Multi-objective evolutionary algorithm; automatically generated natural language medical reports;
Public URL http://researchrepository.napier.ac.uk/id/eprint/9673
Contract Date Mar 16, 2016

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Publisher Licence URL
http://creativecommons.org/licenses/by-nc-nd/4.0/

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