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2017 | OriginalPaper | Chapter

Personality-Dependent Referring Expression Generation

Authors : Ivandré Paraboni, Danielle Sampaio Monteiro, Alex Gwo Jen Lan

Published in: Text, Speech, and Dialogue

Publisher: Springer International Publishing

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Abstract

This paper addresses the issue of how Big Five personality traits may influence the content selection task in Referring Expression generation (REG.) To this end, we build a corpus of referring expressions annotated with personality information, and then use it as the input to a machine learning approach to REG that takes the personality of the target speakers into account. Results show that personality-dependent REG outperforms standard REG algorithms, and that it may be a viable alternative to speaker-dependent approaches that require examples of descriptions produced by every individual under consideration.

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previous chapter Robust Automatic Evaluation of Intelligibility in Voice Rehabilitation Using Prosodic Analysis

next chapter Big Five Personality Recognition from Multiple Text Genres

Stimulus images courtesy of Michael J. Tarr, Center for the Neural Basis of Cognition and Department of Psychology, Carnegie Mellon Univ. Funding provided by NSF award 0339122.

The use of frequency estimates in DT-b5 may in principle defeat the purpose of not relying on pre-recorded examples of referring expressions. In the current DT-b5 implementation, however, these features were included only as a means to provide a meaningful comparison with DT-scene, and could in principle be replaced by a more realistic account of salience.

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Title: Personality-Dependent Referring Expression Generation
Authors: Ivandré Paraboni
Danielle Sampaio Monteiro
Alex Gwo Jen Lan
Publisher: Springer International Publishing
Book: Text, Speech, and Dialogue
Print ISBN: 978-3-319-64205-5

Electronic ISBN: 978-3-319-64206-2

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-64206-2_3

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