Research Progress on Intent Detection Oriented to Transfer Learning

Abstract

Spoken language understanding (SLU) is an important part of human-machine dialogue system. Intent detection is an important sub-task in SLU, because it can expand the fields of dialogue in a limited field. Due to the increase in the demand for dialogue systems in practical application fields, and the new fields that need to be developed cannot obtain a large amount of data in a short time, it poses challenges for building deep learning models in new fields. Transfer learning is a special application of deep learning. In transfer learning, the source and target domains can be used to complete the construction of a target domain model with only a small amount of labeled data. The learning process is completed by transferring knowledge between the source and target domains. Based on labeled data and models in existing fields, building a new dialogue system with only a small amount of labeled data is a current research focus. This paper summarizes the task of intent detection, classifies and elaborates transfer learning methods, and summarizes their problems and solutions. This paper further thinks about how to apply transfer learning to the intent detection task, so as to promote a new field of intent detection research with a small amount of data.

Keywords

• spoken language understanding
• intent detection
• deep learning
• transfer learning