An Empirical Study of Transformer Based Methods for Intent Classification

Xianlin Ding

Abstract:

Intent classification is a fundamental task in natural language processing that aims to determine the intention or purpose behind a given text utterance. The goal is to classify the text into
one or more predefined categories, each representing a specific intent. This task is essential for developing conversational agents, chatbots, and virtual assistants that can understand and respond
appropriately to user queries. Various techniques have been proposed for intent classification, including rule-based methods, machine learning-based models, and deep learning-based approaches.
In this paper, we explore transformer based technics for intent classificaiton.