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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Notes
- 1.
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.
- 2.
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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This work has been supported by FAPESP grant 2016/14223-0.
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Paraboni, I., Monteiro, D.S., Lan, A.G.J. (2017). Personality-Dependent Referring Expression Generation. In: Ekštein, K., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2017. Lecture Notes in Computer Science(), vol 10415. Springer, Cham. https://doi.org/10.1007/978-3-319-64206-2_3
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