When two people pay attention to each other and are interested in what the other has to say or write, they almost instantly adapt their writing/speaking style to match the other. For a successful interaction with a user, chatbots and dialogue systems should be able to do the same. We propose a framework consisting of five psycholinguistic textual characteristics for better human-computer interaction. We describe the annotation processes used for collecting the data, and benchmark five binary classification tasks, experimenting with different training sizes and model architectures. The best architectures noticeably outperform several baselines and achieve macro-averaged F1-scores between 72\% and 96\% depending on the language and the task. The proposed framework proved to be fairly easy to model for various languages even with small amount of manually annotated data if right architectures are used.
@article{arxiv.2012.09692,
title = {Five Psycholinguistic Characteristics for Better Interaction with Users},
author = {Sanja Štajner and Seren Yenikent and Marc Franco-Salvador},
journal= {arXiv preprint arXiv:2012.09692},
year = {2022}
}