Search Results for author:
Found 9 papers, 9 papers with code
MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
We believe that MIntRec2. 0 will serve as a valuable resource, providing a pioneering foundation for research in human-machine conversational interactions, and significantly facilitating related applications.
Token-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent Recognition
To establish an optimal multimodal semantic environment for text modality, we develop a modality-aware prompting module (MAP), which effectively aligns and fuses features from text, video and audio modalities with similarity-based modality alignment and cross-modality attention mechanism.
Ranked #2 on
Multimodal Intent Recognition
on MIntRec
A Clustering Framework for Unsupervised and Semi-supervised New Intent Discovery
New intent discovery is of great value to natural language processing, allowing for a better understanding of user needs and providing friendly services.
MIntRec: A New Dataset for Multimodal Intent Recognition
This paper introduces a novel dataset for multimodal intent recognition (MIntRec) to address this issue.
Ranked #1 on
Multimodal Intent Recognition
on MIntRec
Learning Discriminative Representations and Decision Boundaries for Open Intent Detection
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.
TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition
It is composed of two main modules: open intent detection and open intent discovery.
Deep Open Intent Classification with Adaptive Decision Boundary
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)
Discovering New Intents with Deep Aligned Clustering
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
Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement
Identifying new user intents is an essential task in the dialogue system.
Ranked #1 on
Open Intent Discovery
on ATIS
