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

• URL: http://arxiv.org/abs/2307.05174v1
• Date: Tue, 11 Jul 2023 11:12:06 GMT
• Title: 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
• Authors: Che Zhang and Ping'an Liu and Zhenyang Xiao and Haojun Fei
• Abstract summary: SemEval 2023 Task 4citekiesel:2023 provides a set of arguments and 20 types of human values implicitly expressed in each argument. We propose a multi-head attention mechanism to establish connections between specific labels and semantic components. Our approach achieved an F1 score of 0.533 on the test set and ranked fourth on the leaderboard.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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\cite{kiesel: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\cite{liu_roberta_2019} 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\cite{su_contrastive_2022} 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.

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