Search Results for author:
Found 6 papers, 2 papers with code
Efficient data selection employing Semantic Similarity-based Graph Structures for model training
The results indicate reliable projections of ASR performance, with a 93% accuracy increase when using the proposed method compared to random predictions, bringing non-trivial information on the impact of textual representations in speech models.
Graph Neural Network Enhanced Language Models for Efficient Multilingual Text Classification
To overcome these challenges, we propose a multilingual disaster related text classification system which is capable to work under \{mono, cross and multi\} lingual scenarios and under limited supervision.
Supervised Graph Contrastive Pretraining for Text Classification
In this paper, we propose a novel way to effectively utilize labeled data from related tasks with a graph based supervised contrastive learning approach.
Unsupervised Domain Adaptation with Global and Local Graph Neural Networks in Limited Labeled Data Scenario: Application to Disaster Management
To handle this challenge, we utilize limited labeled data along with abundantly available unlabeled data, generated during a source disaster to propose a novel two-part graph neural network.
Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild
With the exponential growth of online marketplaces and user-generated content therein, aspect-based sentiment analysis has become more important than ever.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Logic Constrained Pointer Networks for Interpretable Textual Similarity
We study the problem of aligning components of sentences leading to an interpretable model for semantic textual similarity.
