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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