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%.

翻译：我们描述了针对SemEval-2023任务4（识别论证背后的人类价值观，ValueEval）所开展的实验。由于人类价值观是主观概念，需要精确的定义，因此我们假设在模型训练过程中融入人类价值观的定义（以注释说明和经验证的问卷题项形式）能够提升预测性能。我们对这一思路进行了探索，结果表明我们提出的模型表现优于挑战组织者提供的基线模型，宏F1分数提升幅度高达18%。