Search Results for author:
Found 10 papers, 2 papers with code
Text Intimacy Analysis using Ensembles of Multilingual Transformers
In this paper, we present our work on the SemEval shared task 9 on predicting the level of intimacy for the given text.
Empathy and Distress Detection using Ensembles of Transformer Models
The provided dataset consists of several long-text examples in the English language, with each example associated with a numeric score for empathy and distress.
Mavericks at BLP-2023 Task 1: Ensemble-based Approach Using Language Models for Violence Inciting Text Detection
This paper presents our work for the Violence Inciting Text Detection shared task in the First Workshop on Bangla Language Processing.
SenTest: Evaluating Robustness of Sentence Encoders
The results of the experiments strongly undermine the robustness of sentence encoders.
My Boli: Code-mixed Marathi-English Corpora, Pretrained Language Models and Evaluation Benchmarks
This is the first work that presents artifacts for code-mixed Marathi research.
A Twitter BERT Approach for Offensive Language Detection in Marathi
The MahaTweetBERT, a BERT model, pre-trained on Marathi tweets when fine-tuned on the combined dataset (HASOC 2021 + HASOC 2022 + MahaHate), outperforms all models with an F1 score of 98. 43 on the HASOC 2022 test set.
Large Language Models for Multi-label Propaganda Detection
The spread of propaganda through the internet has increased drastically over the past years.
Spread Love Not Hate: Undermining the Importance of Hateful Pre-training for Hate Speech Detection
Pre-training large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks.
Software-Level Accuracy Using Stochastic Computing With Charge-Trap-Flash Based Weight Matrix
The in-memory computing paradigm with emerging memory devices has been recently shown to be a promising way to accelerate deep learning.
Band-to-Band Tunneling based Ultra-Energy Efficient Silicon Neuron
Neuromorphic engineering implements SNNs in hardware, aspiring to mimic the brain at scale (i. e., 100 billion neurons) with biological area and energy efficiency.
