Elsevier

Information Fusion

Volume 94, June 2023, Pages 43-65
Information Fusion

Full length article
Human-centered neural reasoning for subjective content processing: Hate speech, emotions, and humor

https://doi.org/10.1016/j.inffus.2023.01.010Get rights and content
Under a Creative Commons license
open access

Highlights

  • Human-centered neural architectures suitable for subjective NLP problems are introduced.

  • Personalized NLP requires dedicated validation procedures.

  • Personalized methods revealed their superiority over generalized approaches for 14 tasks related to hate speech, emotions and humor.

  • Language models, multi-tasking and fine-tuning have less impact than personalization.

  • There is correlation between formula-based human bias and bias learned by the neural model.

Abstract

Some tasks in content processing, e.g., natural language processing (NLP), like hate or offensive speech and emotional or funny text detection, are subjective by nature. Each human may perceive some content individually. The existing reasoning methods commonly rely on agreed output values, the same for all recipients. We propose fundamentally different — personalized solutions applicable to any subjective NLP task. Our five new deep learning models take into account not only the textual content but also the opinions and beliefs of a given person. They differ in their approaches to learning Human Bias (HuBi) and fusion with content (text) representation. The experiments were carried out on 14 tasks related to offensive, emotional, and humorous texts. Our personalized HuBi methods radically outperformed the generalized ones for all NLP problems. Personalization also has a greater impact on reasoning quality than commonly explored pre-trained and fine-tuned language models. We discovered a high correlation between human bias calculated using our dedicated formula and that learned by the model. Multi-task solutions achieved better outcomes than single-task architectures. Human and word embeddings also provided additional insights.

Keywords

Content perception
NLP
Subjective NLP tasks
Personalized NLP
Offensive content
Hate speech
Emotion recognition
Humor detection
Learning human representations
Human bias
Text classification
Information fusion

Data availability

We have shared the link to our source code for experiments in Section 8: Experimental Setup.

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