New intent discovery is of great value to natural language processing, allowing for a better understanding of user needs and providing friendly services.
This paper introduces a novel dataset for multimodal intent recognition (MIntRec) to address this issue.
Ranked #1 on Multimodal Intent Recognition on MIntRec
To address these issues, this paper presents an original framework called DA-ADB, which successively learns distance-aware intent representations and adaptive decision boundaries for open intent detection.
It is composed of two main modules: open intent detection and open intent discovery.
In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification.
Ranked #1 on Open Intent Detection on StackOverFlow(75%known)
In this work, we propose an effective method, Deep Aligned Clustering, to discover new intents with the aid of the limited known intent data.
Ranked #2 on Open Intent Discovery on CLINC150
Identifying new user intents is an essential task in the dialogue system.
Ranked #1 on Open Intent Discovery on ATIS