Five Psycholinguistic Characteristics for Better Interaction with Users

TL;DR

This paper introduces a framework of five psycholinguistic features to enhance human-computer interaction, demonstrating effective classification across multiple languages with minimal annotated data.

Contribution

It proposes a novel set of psycholinguistic characteristics and benchmarks their effectiveness in binary classification tasks for better dialogue system interaction.

Findings

Best models achieve macro F1-scores between 72% and 96%.

Framework is adaptable to various languages with limited annotated data.

Models outperform several baselines.

Abstract

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 F$_1$-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.