3 citations · 4 across the 6 of their papers we have counts for
6 papers
CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP
Chandra Kiran Reddy Evuru, Sreyan Ghosh, Sonal Kumar +3
We present CoDa (Constrained Generation based Data Augmentation), a controllable, effective, and training-free data augmentation technique for low-resource (data-scarce) NLP. Our a…
Emotion-Aware Multimodal Fusion for Meme Emotion Detection
Shivam Sharma, Ramaneswaran S, Md. Shad Akhtar +1
The ever-evolving social media discourse has witnessed an overwhelming use of memes to express opinions or dissent. Besides being misused for spreading malcontent, they are mined b…
DALE: Generative Data Augmentation for Low-Resource Legal NLP
Sreyan Ghosh, Chandra Kiran Evuru, Sonal Kumar +4
We present DALE, a novel and effective generative Data Augmentation framework for low-resource LEgal NLP. DALE addresses the challenges existing frameworks pose in generating effec…
From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues
Shivani Kumar, Ramaneswaran S, Md Shad Akhtar +1
Understanding emotions during conversation is a fundamental aspect of human communication, driving NLP research for Emotion Recognition in Conversation (ERC). While considerable re…
Composite Diffusion | whole >= Σparts
Vikram Jamwal, Ramaneswaran S
For an artist or a graphic designer, the spatial layout of a scene is a critical design choice. However, existing text-to-image diffusion models provide limited support for incorpo…
ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER
Sreyan Ghosh, Utkarsh Tyagi, Manan Suri +3
Complex Named Entity Recognition (NER) is the task of detecting linguistically complex named entities in low-context text. In this paper, we present ACLM Attention-map aware keywor…