no code implementations • 22 Feb 2024 • Roxana Petcu, Subhadeep Maji
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.
no code implementations • 6 Mar 2022 • Samujjwal Ghosh, Subhadeep Maji, Maunendra Sankar Desarkar
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.
no code implementations • 21 Dec 2021 • Samujjwal Ghosh, Subhadeep Maji, Maunendra Sankar Desarkar
In this paper, we propose a novel way to effectively utilize labeled data from related tasks with a graph based supervised contrastive learning approach.
no code implementations • 3 Apr 2021 • Samujjwal Ghosh, Subhadeep Maji, Maunendra Sankar Desarkar
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.
1 code implementation • 23 Jan 2021 • Rajdeep Mukherjee, Shreyas Shetty, Subrata Chattopadhyay, Subhadeep Maji, Samik Datta, Pawan Goyal
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)
1 code implementation • 15 Jul 2020 • Subhadeep Maji, Rohan Kumar, Manish Bansal, Kalyani Roy, Pawan Goyal
We study the problem of aligning components of sentences leading to an interpretable model for semantic textual similarity.