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
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 a...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
11.07.2023
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Subjects | |
Online Access | Get full text |
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Summary: | 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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DOI: | 10.48550/arxiv.2307.05174 |