Sina at S em E val-2023 Task 4: A Class-Token Attention-based Model for Human Value Detection (2023)

Abstract: The human values expressed in argumentative texts can provide valuable insights into the culture of a society. They can be helpful in various applications such as value-based profiling and ethical analysis. However, one of the first steps in achieving this goal is to detect the category of human value from an argument accurately. This task is challenging due to the lack of data and the need for philosophical inference. It also can be challenging for humans to classify arguments according to their underlying human values. This paper elaborates on our model for the SemEval 2023 Task 4 on human value detection. We propose a class-token attention-based model and evaluate it against baseline models, including finetuned BERT language model and a keyword-based approach.

## introduction the social sciences and humanities provide insight into understanding the world and its people, with a primary responsibility of solving human-based issues and providing recommendations. the study of human argumentation and causality is an approach that aids in understanding human relationships and culture, with applications in areas such as faceted search @xcite , valuebased argument generation @xcite , and value-based personality profiling @xcite . the semantic evaluation 2023 includes the human value detection task @xcite , which aims to classify the human value category based on textual argument. our study investigates the effectiveness of keyword extraction and attention-based neural models. our findings indicate that while context keywords contain the primary argument value, incorporating the class embedding of arguments as queries that * equal contribution focus on the most important concepts of each argument can improve classification results. we also observed that simple models like svm @xcite perform well compared to neural networks due to the dataset's small size, multi-class prediction, and numerous labels. we participated in the human value detection task at tira @xcite and achieved an average score of 0.47 for all labels, which was 0.09 less than the first team's score. however, we attained the best f1 score of 0.54 for the power: resources label, which was 0.01 better than the top-performing approach. these findings suggest that there is room for improvement in the task. to facilitate easier evaluation and reproducibility of our results, we have made our baselines and proposed models foot_0 available open-source as several jupyter-notebooks and docker images. ## background human values are ubiquitous in social sciences, and identifying them in argumentative texts can help understand cultures, conflicting beliefs, and opinions. in the human value detection classification task, it is required to determine human values, given human arguments containing premises and conclusions @xcite . ## system overview this section provides a review of the baseline methods and the proposed methods for addressing the task at hand, including keyword extraction and attention-based models. ## results for nahj al-balagha data, the result of our model was better than the 1-baseline; it could not improve the bert baseline, and to improve the result, we need more related human-value data from a religious source. ## conclusion detecting human values is a practical task that challenges natural language processing methods because of the necessity to comprehend moral and philosophical concepts. this paper proposes a solution to this problem by learning class-specific embedding and utilizing an attention mechanism to find the best features for a binary classifier. although we have introduced novel models to address this issue, we have not achieved the desired level of accuracy because we used bert-base instead of models with more parameters and a single transformer-based model due to our computational resource constraints. given the paucity of data for numerous classes, unsupervised techniques such as training a dedicated language model for philosophical and moral text could be utilized in future research.