Intent Classification Based on Single Model

Intent classification is an important preprocessing step in natural language processing tasks, used to categorize input text into specific intents, in order to assign them to corresponding subsystems or processing flows. In dialogue systems, intent classification is used to identify the intentions of users, so as to respond accordingly to their needs. In previous research, various types of neural network architectures have been proposed for intent classification tasks, including feedforward neural networks and recurrent neural networks (RNNs). This chapter provides a comprehensive introduction and comparison of two common RNN architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). LSTM and GRU are two common RNN architectures used for intent classification tasks and intent recognition in dialogue systems. They perform well in handling long text sequences and large dialogue datasets, and have their own advantages and applicable scenarios. The choice between these models depends on specific task requirements, dataset size, computational resources, and other factors.