← Back to papers
2023 SEMEVAL SemEval 2023

Quintilian at SemEval-2023 Task 4: Grouped BERT for Multi-Label Classification

Abstract

AbstractIn this paper, we initially discuss about the ValueEval task and the challenges involved in multi-label classification tasks. We tried to approach this task using Natural Language Inference and proposed a Grouped-BERT architecture which leverages commonality between the classes for a multi-label classification tasks.

🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Ajay Narasimha Mopidevi , Hemanth Chenna

Topics

Machine Learning > Core Methods > Classification Natural Language Processing > Resources & Methods > Natural Language Inference Machine Learning > Learning Types > Transfer Learning

Keywords

transfer learning natural language inference multi-label classification bert model
Download PDF

Related papers

Coco at SemEval-2023 Task 10: Explainable Detection of Online Sexism 2023
ZBL2W at SemEval-2023 Task 9: A Multilingual Fine-tuning Model with Data Augmentation for Tweet Intimacy Analysis 2023
MLModeler5 at SemEval-2023 Task 3: Detecting the Category and the Framing Techniques in Online News in a Multi-lingual Setup 2023
OPI at SemEval-2023 Task 9: A Simple But Effective Approach to Multilingual Tweet Intimacy Analysis 2023
NLP-LISAC at SemEval-2023 Task 12: Sentiment Analysis for Tweets expressed in African languages via Transformer-based Models 2023