Multi-task Learning for Named Entity Recognition and Intent Classification in Natural Language Understanding Applications

Background: Understanding human language is a part of the research in Natural Language Processing (NLP) known as Natural Language Understanding (NLU). It becomes a crucial part of some NLP applications such as chatbots, that interpret the user intent and important entities. NLU systems depend on intent classification and named entity recognition (NER) which is crucial for understanding the user input to extract meaningful information. Not only important in chatbots, NLU also provides a pivotal function in other applications for efficient and precise text understanding. Objective: The aim of this study is to introduce multitask learning techniques to improve the application's performance on NLU tasks, especially intent classification and NER in specific domains. Methods: To achieve the language understanding capability, a strategy is to combine the intent classification and entity recognition tasks by using a shared model based on the shared representation and task dependencies. This approach is known as multitask learning and leverages the collaborative interaction between these related tasks to enhance performance. The proposed learning architecture is designed to be adaptable to various NLU-based applications, but in this work are discussed use cases in chatbots. Results: The results show the effectiveness of the proposed approach by following several experiments, both from intent classification and named entity recognitions. The multitask learning capabilities highlight the potential of multi-task learning in chatbot systems for close domains. The optimal hyperparameters consist of a warm-up step of 60, an early stopping probability of 10, a weight decay of 0.001, a Named Entity Recognition (NER) loss weight of 0.58, and an intention classification loss weight of 0.4. Conclusion: The performance of Dual Intent and Entity Transformer (DIET) for both tasks—intent classification and named entity recognition—is highly dependent on the data. This leads to various capabilities for the hyperparameter combinations. Our proposed model architecture significantly outperforms previous studies based on common evaluation metrics. Keywords: Natural Language Understanding, Chatbot, Multi-task Learning, Named Entity Recognition