Personalized and Subjective approaches to Natural Language Processing

Introduction

My project, PERSONAE, will make language technology (LT) accessible and valuable to everyone. I will revolutionize research in subjective tasks in NLP such as abusive language detection and sentiment and emotion analysis by developing a new field called personal NLP, yielding new datasets, tasks, and algorithms. This new research area will explore subjective tasks from the perspective of the individual as information receiver, making users active actors in the creation of LTs instead of mere recipients. This will allow for a more tailored, effective approach to NLP model design, resulting in better models overall.

Individual Preferences

Each person has their own interests and preferences based on their background and experience. These factors impact their views of what makes them happy, angry, or depressed over time. Language technologies (LTs) can consider individual preferences. However, current research presumes a static view of subjectivity: that a single ground truth underlies subjective tasks such as abusive language detection, an assumption that lacks human variability and prevents universal access to LTs.

Current Limitations of Language-based AI

Language-based AI such as virtual assistants is widely available. But despite significant scientific advances, most LT applications are inaccessible to individuals and their public's opinion has become increasingly negative.

1. GPT-3's 2020 release boosted business-oriented applications such as copywriting and chatbots.
2. Few applications allow people to improve their lives—for example, by controlling what they see on social media.

This gap becomes more pronounced for subjective tasks.

Future Directions

I will design subjective LTs that can be adapted by individuals at will over time. Based on an ambitious meta approach able to generalize from existing, disconnected work, PERSONAE will rely on fully personalizable privacy-aware algorithms that can be used by anyone. It will reveal benefits of LT far beyond those of existing systems, paving the way for future applications.