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Found 5 papers, 5 papers with code
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions
We present ABEX, a novel and effective generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks.
DALE: Generative Data Augmentation for Low-Resource Legal NLP
We present DALE, a novel and effective generative Data Augmentation framework for low-resource LEgal NLP.
ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER
In addition, we demonstrate the application of ACLM to other domains that suffer from data scarcity (e. g., biomedical).
M-MELD: A Multilingual Multi-Party Dataset for Emotion Recognition in Conversations
Expression of emotions is a crucial part of daily human communication.
MMER: Multimodal Multi-task Learning for Speech Emotion Recognition
In this paper, we propose MMER, a novel Multimodal Multi-task learning approach for Speech Emotion Recognition.
Ranked #3 on
Multimodal Emotion Recognition
on IEMOCAP
(Weighted Accuracy (WA) metric, using extra
training data)
