22 citations · 30 across the 6 of their papers we have counts for
6 papers
Universal Semantic Disentangled Privacy-preserving Speech Representation Learning
Biel Tura Vecino, Subhadeep Maji, Aravind Varier +11
The use of audio recordings of human speech to train LLMs poses privacy concerns due to these models' potential to generate outputs that closely resemble artifacts in the training…
Efficient data selection employing Semantic Similarity-based Graph Structures for model training
Roxana Petcu, Subhadeep Maji
Recent developments in natural language processing (NLP) have highlighted the need for substantial amounts of data for models to capture textual information accurately. This raises…
Graph Neural Network Enhanced Language Models for Efficient Multilingual Text Classification
Samujjwal Ghosh, Subhadeep Maji, Maunendra Sankar Desarkar
Online social media works as a source of various valuable and actionable information during disasters. These information might be available in multiple languages due to the nature…
Supervised Graph Contrastive Pretraining for Text Classification
Samujjwal Ghosh, Subhadeep Maji, Maunendra Sankar Desarkar
Contrastive pretraining techniques for text classification has been largely studied in an unsupervised setting. However, oftentimes labeled data from related tasks which share labe…
Unsupervised Domain Adaptation with Global and Local Graph Neural Networks in Limited Labeled Data Scenario: Application to Disaster Management
Samujjwal Ghosh, Subhadeep Maji, Maunendra Sankar Desarkar
Identification and categorization of social media posts generated during disasters are crucial to reduce the sufferings of the affected people. However, lack of labeled data is a s…
Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild
Rajdeep Mukherjee, Shreyas Shetty, Subrata Chattopadhyay +3
With the exponential growth of online marketplaces and user-generated content therein, aspect-based sentiment analysis has become more important than ever. In this work, we critica…