Personality-dependent content selection in natural language generation systems

Abstract

This paper focuses on the computer side of human-computer interaction through natural language, which is the domain of natural language generation (NLG) studies. From a given (usually non-linguistic) input, NLG systems will in principle generate the same fixed text as an output and in order to attain more natural or human-like interaction will often resort to a wide range of strategies for stylistic variation. Among these, the use of computational models of human personality has emerged as a popular alternative in the field and will be the focus of the present work as well. More specifically, the present study describes two machine learning experiments to establish possible relations between personality and content selection (as opposed to the more well-documented relation between personality and surface realisation), and it is, to the best of our knowledge, the first of its kind to address this issue at both macro and micro planning levels, which may arguably pave the way for the future development of more robust personality-dependent systems of this kind.

Authorship attribution using author profiling classifiers

“…The present analysis makes use of the b5-corpus of Facebook texts described in Ramos et al . (2018), which will be further discussed in Section 4.1 as part of our main author profiling experiments. In this experiment, binary gender labels (male/female) available from the corpus are added as a fourth information source to the authorship attribution ensemble in Custódio and Paraboni (2019), that is, in addition to the word, character and text distortion channels described in the previous Section 2.3.…”

“…2006), Facebook posts from the b5-post corpus (Ramos et al . 2018), short essay texts about topics of a moral nature (e.g., abortion legalisation, death penalty, etc.) from the BRmoral corpus (dos Santos and Paraboni 2019; Pavan et al .…”

Towards corpora creation from social web in Brazilian Portuguese to support public security analyses and decisions

“…According to the authors, this corpus is suitable for topic mining with broad applications. Ramos et al (2019) created a corpus containing texts from different communicative tasks and the personality descriptions of their authors. It is a topic-free corpus, useful for computational studies about recognition of human personality, author profiling, and text generation based on personality traits, for instance.…”

Application Analysis of Multimedia Technology in College English Curriculum

“…For example, used arti cial neural networks to explore the network learning ability of four types of verbs, compared the performance of a single-layer perceptron with the multilayer perceptron used by Rumelhart and McClelland, and outlined the performance of several systems. U-shaped learning performance, or the deep systematic learning mode, is a theory of computer learning process based on comprehension and explores the many ways in which multilayer networks capture the characteristics of children's U-shaped learning; Michael S. C. omas and Annette Karmilo -Smith use computational models to study language developmental disorders in children with Williams syndrome [3].…”