Hierarchical and Bidirectional Joint Multi-Task Classifiers for Natural Language Understanding

Xiaoyu Ji, Wanyang Hu () and Yanyan Liang ()
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Xiaoyu Ji: School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China
Wanyang Hu: Faculty of Informatics, Università della Svizzera Italiana, 6962 Lugano, Switzerland
Yanyan Liang: School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China

Mathematics, 2023, vol. 11, issue 24, 1-22

Abstract: The MASSIVE dataset is a spoken-language comprehension resource package for slot filling, intent classification, and virtual assistant evaluation tasks. It contains multi-language utterances from human beings communicating with a virtual assistant. In this paper, we exploited the relationship between intent classification and slot filling to improve the exact match accuracy by proposing five models with hierarchical and bidirectional architectures. There are two variants for hierarchical architectures and three variants for bidirectional architectures. These are the hierarchical concatenation model, the hierarchical attention-based model, the bidirectional max-pooling model, the bidirectional LSTM model, and the bidirectional attention-based model. The results of our models showed a significant improvement in the averaged exact match accuracy. The hierarchical attention-based model improved the accuracy by 1.01 points for the full training dataset. As for the zero-shot setup, we observed that the exact match accuracy increased from 53.43 to 53.91. In this study, we observed that, for multi-task problems, utilizing the relevance between different tasks can help in improving the model’s overall performance.

Keywords: multi-task classifier; hierarchical structure; bidirectional joint structure; MASSIVE dataset (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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