Joint model for intent and entity recognition
This addresses the problem of resource inefficiency in semantic understanding for dialogue systems, though it appears incremental as it builds on existing joint modeling concepts.
The paper tackles the joint modeling of intent classification and entity detection in natural dialogues, achieving improved metrics for both tasks with reduced training requirements compared to separate approaches.
The semantic understanding of natural dialogues composes of several parts. Some of them, like intent classification and entity detection, have a crucial role in deciding the next steps in handling user input. Handling each task as an individual problem can be wasting of training resources, and also each problem can benefit from each other. This paper tackles these problems as one. Our new model, which combine intent and entity recognition into one system, is achieving better metrics in both tasks with lower training requirements than solving each task separately. We also optimize the model based on the inputs.