SUTNLP at S em E val-2023 Task 4: LG -Transformer for Human Value Detection (2023)

Abstract: When we interact with other humans, humanvalues guide us to consider the human element. As we shall see, value analysis in NLP hasbeen applied to personality profiling but not toargument mining. As part of SemEval-2023Shared Task 4, our system paper describes amulti-label classifier for identifying human val-ues. Human value detection requires multi-label classification since each argument maycontain multiple values. In this paper, we pro-pose an architecture called Label Graph Trans-former (LG-Transformer). LG-Transformeris a two-stage pipeline consisting of a trans-former jointly encoding argument and labelsand a graph module encoding and obtainingfurther interactions between labels. Using ad-versarial training, we can boost performanceeven further. Our best method scored 50.00 us-ing F1 score on the test set, which is 7.8 higherthan the best baseline method. Our code ispublicly available on Github.

## introduction most, if not all, social sciences stress the importance of human values @xcite and have integrated them into computational frameworks of argumentation @xcite . the analysis of values in nlp has been applied to personality profiling but hasn't been applied to argument mining @xcite . values are commonly accepted answers to why some option is desirable in the ethical sense and are thus essential both in real-world argumentation and theoretical argumentation frameworks @xcite . for identifying human values behind arguments, touché23-valueeval collected 9,324 arguments from 6 diverse sources, including religious texts, political discussions, free-text arguments, newspaper editorials, and online democracy platforms @xcite . the purpose of this system paper is to present sutnlp's (david gauthier on leaderboard) work on the semeval-2023 shared task 4 which is focused on developing a classifier to classify human values @xcite . detecting human values requires multi-label classification since each argument may contain several values. to tackle this problem, we propose a two-stage pipeline consisting of (1) a transformer @xcite which jointly encodes the argument and labels and (2) a graph module @xcite which further encodes and gets interactions between labels. lastly, we employ adversarial training @xcite to further enhance the model's performance. the best performing method achieves f1 score of 50.00 % on the test set, outperforming the best baseline method by 7.8 %. using our method, we ranked 8th out of 40 competing teams. results are submitted through tira @xcite . ## background in human value detection task, a textual argument and multiple human value categories are given and the goal is to classify which categories, the argument draws on. this task uses a set of 20 value categories compiled from the social science and described in identifying the human values behind arguments paper @xcite . the value categories are shown in figure 1 . the main focus of human value detection has been on developing multi-label text classification @xcite ). text classification is a fundamental task in natural language processing (nlp), and multi-label text classification (mltc) is a key branch of it. mltc has undergone a transformation from traditional machine learning to deep learning, and various models with excellent performance have emerged one after another @xcite . recently researches have shown that pre-trained language models like transformers approach have figure 1 : the employed value taxonomy of 20 value categories @xcite proven to be effective in multi-class text classification @xcite . the bert model @xcite has emerged as a popular state-ofthe-art model in recent years. it is able to cope with nlp tasks such as multi-label text classification @xcite . transformer models have become the most effective neural network architecture for neural language modeling. a novel model architecture called deberta (decoding-enhanced bert with disentangled attention) improves the bert model using two novel techniques. the first is the disentangled attention mechanism, where each token is represented using two vectors that encode its content and position respectively. the attention weights among tokens are computed using disentangled matrices on their contents and relative positions respectively. second, an enhanced mask decoder is used to incorporate absolute positions in the decoding layer to predict the masked tokens in model pre-training @xcite . a sample of text can be assigned to more than one classification in multi-label text classification (mltc). since in mltc tasks, there are dependencies or correlations among labels, in recent years several models have been proposed to captures the dependencies between labels. in a recent work, a graph attention network-based model is proposed to handle the attentive dependency structure among the labels. the graph attention network uses a feature matrix and a correlation matrix to model and explore the crucial dependencies between the labels and generate classifiers @xcite . @xcite proposes a label-specific dual graph neural network, which incorporates labels information to learn label-specific components from documents, and employs dual graph convolution network to model interactions among these components based on co-occurrence and dynamic reconstruction graph in a joint way. @xcite jointly encodes text and labels to exploit labels' semantics, to model correlation between labels. aside from classifying labels, they further define additional targets. @xcite also uses labels' semantics and they capture labels' semnatic in two stages. a label graph construction approach is proposed to capture the label correlations and a neoteric attention mechanism to establish the semantic connections between labels and words and to obtain the label-specific word representation. in designing a machine learning method, generalization and robustness are both critical requirements. adversarial training is a means to enhance robustness and generalization @xcite . in natural language processing (nlp), pre-training large neural language models such as bert have demonstrated impressive gain in generalization for a variety of tasks, with further improvement from adversarial fine-tuning @xcite . in recent years, adversarial training has gained popularity in the field of natural language processing (nlp). @xcite applies adversarial training to improve the performance of semi-supervised text classification models. the method has been applied to a variety of nlp applications, including sentiment analysis, spam detection, and topic modeling @xcite @xcite . further, performance on several benchmarks in nlp have been improved by deploying adversarial training @xcite . ## conclusion the paper presents sutnlp's submission to semeval-2023 task 4 "valueeval: identification of human values behind arguments" competition. to solve this problem, we use transformers to jointly encode premise and labels and exploit the semantic correlation between label names and between label names and the premise. further we propose a label graph to enhance labels interaction and further process label representations. additionally we employ adversarial training to further boost the performance of the model. through experiments, we show that using label graph is imperative for the task when using label semantics. mean of f1 scores over labels is used to compare the performance of the models. by this criteria our proposed method proves to be effective and our best model outperforms the bert baseline by 7.8 %.