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

arXiv: https://arxiv.org/abs/2302.13925 · HTML: https://mlsanity.com/paper/2302.13925/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Macro F1 and Macro F1](https://mlsanity.com/paper/1911.03347/) (2019)
- [Learning to Recognize Dialect Features](https://mlsanity.com/paper/2010.12707/) (2020)
- [The Touch'e23-ValueEval Dataset for Identifying Human Values behind Arguments](https://mlsanity.com/paper/2301.13771/) (2023)
- [Evaluating the Construct Validity of Text Embeddings with Application to Survey Questions](https://mlsanity.com/paper/2202.09166/) (2022)
- [Items from Psychometric Tests as Training Data for Personality Profiling Models of Twitter Users](https://mlsanity.com/paper/2202.10415/) (2022)
- [Improving Stance Detection by Leveraging Measurement Knowledge from Social Sciences: A Case Study of Dutch Political Tweets and Traditional Gender Role Division](https://mlsanity.com/paper/2212.06543/) (2022)

## Built on by (most cited first, 4 of 4)

- [A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practice](https://mlsanity.com/paper/2404.16958/) (2024)
- [Value Lens: Using Large Language Models to Understand Human Values](https://mlsanity.com/paper/2512.15722/) (2025)
- [More Context, Larger Models, or Moral Knowledge? A Systematic Study of Schwartz Value Detection in Political Texts](https://mlsanity.com/paper/2605.22641/) (2026)
- [Identifying and Understanding Human Values in Text: A Tailorable LLM-based Architecture](https://mlsanity.com/paper/2605.27373/) (2026)

## Abstract (arXiv)

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%.