Search Results for author:
Found 10 papers, 2 papers with code
Towards Capturing Changes in Mood and Identifying Suicidality Risk
This paper describes our systems for CLPsych? s 2022 Shared Task.
IIITH at SemEval-2022 Task 5: A comparative study of deep learning models for identifying misogynous memes
This paper provides a comparison of different deep learning methods for identifying misogynous memes for SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification.
Leveraging Mental Health Forums for User-level Depression Detection on Social Media
We leverage accounts of depression taken from this dataset to infuse domain-specific elements into our framework.
Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches
This study presents a comprehensive analysis and comparison of two predominant fine-tuning methodologies - full-parameter fine-tuning and parameter-efficient tuning - within the context of medical Large Language Models (LLMs).
Neural models for Factual Inconsistency Classification with Explanations
The proposed system first predicts inconsistent spans from claim and context; and then uses them to predict inconsistency types and inconsistent entity types (when inconsistency is due to entities).
IIITH at SemEval-2021 Task 7: Leveraging transformer-based humourous and offensive text detection architectures using lexical and hurtlex features and task adaptive pretraining
This paper describes our approach (IIITH) for SemEval-2021 Task 5: HaHackathon: Detecting and Rating Humor and Offense.
Identifying COVID-19 Fake News in Social Media
The evolution of social media platforms have empowered everyone to access information easily.
Task Adaptive Pretraining of Transformers for Hostility Detection
Identifying adverse and hostile content on the web and more particularly, on social media, has become a problem of paramount interest in recent years.
Leveraging Multilingual Transformers for Hate Speech Detection
Our work leverages state of the art Transformer language models to identify hate speech in a multilingual setting.
Development of POS tagger for English-Bengali Code-Mixed data
The proposed system is a modular approach that starts by tagging individual tokens with their respective languages and then passes them to different POS taggers, designed for different languages (English and Bengali, in our case).
