# Epicurus at SemEval-2023 Task 4: Improving Prediction of Human Values behind Arguments by Leveraging Their Definitions

## TL;DR

This paper presents a method for predicting human values behind arguments by incorporating their definitions into model training, resulting in significant performance improvements over baseline models.

## Contribution

The paper introduces a novel approach that leverages explicit definitions of human values to enhance prediction accuracy in argument analysis tasks.

## Key findings

- Models outperform baselines with up to 18% macro F1 score improvement.
- Incorporating value definitions improves prediction performance.
- Using annotated instructions and survey items aids in modeling subjective concepts.

## Abstract

We describe our experiments for SemEval-2023 Task 4 on the identification of human values behind arguments (ValueEval). Because human values are subjective concepts which require precise definitions, we hypothesize that incorporating the definitions of human values (in the form of annotation instructions and validated survey items) during model training can yield better prediction performance. We explore this idea and show that our proposed models perform better than the challenge organizers' baselines, with improvements in macro F1 scores of up to 18%.