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Found 9 papers, 1 papers with code
DialogueTRM: Exploring Multi-Modal Emotional Dynamics in a Conversation
Emotion dynamics formulates principles explaining the emotional fluctuation during conversations.
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification
We construct a prime-dual network structure for few-shot learning which establishes a commutative relationship between the support set and the query set, as well as a new self- supervision constraint for highly effective few-shot learning.
A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification
Previous studies mostly use a fine-tuned Language Model (LM) to strengthen the constraints but ignore the fact that the potential of diversity could improve the effectiveness of generated data.
Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup
To address these issues, we propose the Adversarial Mixing Policy (AMP), organized in a min-max-rand formulation, to relax the Locally Linear Constraints in Mixup.
DialogueTRM: Exploring the Intra- and Inter-Modal Emotional Behaviors in the Conversation
Emotion Recognition in Conversations (ERC) is essential for building empathetic human-machine systems.
Ranked #7 on
Emotion Recognition in Conversation
on IEMOCAP
Research on multi-dimensional end-to-end phrase recognition algorithm based on background knowledge
The algorithm can not only introduce background knowledge, recognize all kinds of nested phrases in sentences, but also recognize the dependency between phrases.
Research on Annotation Rules and Recognition Algorithm Based on Phrase Window
At present, most Natural Language Processing technology is based on the results of Word Segmentation for Dependency Parsing, which mainly uses an end-to-end method based on supervised learning.
Hierarchical Context Enhanced Multi-Domain Dialogue System for Multi-domain Task Completion
Task 1 of the DSTC8-track1 challenge aims to develop an end-to-end multi-domain dialogue system to accomplish complex users' goals under tourist information desk settings.
