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

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.