Mao-Zedong at SemEval-2023 Task 4: Label Represention Multi-Head Attention Model with Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification

Abstract

The study of human values is essential in both practical and theoretical domains.With the development of computational linguistics, the creation of large-scale datasets has made it possible to automatically recognize human values accurately.SemEval 2023 Task 4(Kiesel et al., 2023) provides a set of arguments and 20 types of human values that are implicitly expressed in each argument.In this paper, we present our team's solution.We use the Roberta(Liu et al.) model to obtain the word vector encoding of the document and propose a multi-head attention mechanism to establish connections between specific labels and semantic components.Furthermore, we use a contrastive learning-enhanced K-nearest neighbor mechanism(Su et al.) to leverage existing instance information for prediction.Our approach achieved an F1 score of 0.533 on the test set and ranked fourth on the leaderboard.we make our code publicly available at https://github.com/peterlau0626/semeval2023-task4-HumanValue.