← Back to papers
2023 ACL ACL 2023

Tenzin-Gyatso at SemEval-2023 Task 4: Identifying Human Values behind Arguments Using DeBERTa

Abstract

AbstractIdentifying human values behind arguments isa complex task which requires understandingof premise, stance and conclusion together. Wepropose a method that uses a pre-trained lan-guage model, DeBERTa, to tokenize and con-catenate the text before feeding it into a fullyconnected neural network. We also show thatleveraging the hierarchy in values improves theperformance by .14 F1 score.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
🐝 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

Pavan Kandru , Bhavyajeet Singh , Ankita Maity , Kancharla Aditya Hari , Vasudeva Varma

Topics

Artificial Intelligence > Learning Paradigms > Transfer Learning Machine Learning > Core Methods > Classification

Keywords

transfer learning text classification text encoding neural network
Download PDF

Related papers

Helping a Friend or Supporting a Cause? Disentangling Active and Passive Cosponsorship in the U.S. Congress 2023
History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling 2023
Efficient Transformers with Dynamic Token Pooling 2023
HHU at SemEval-2023 Task 3: An Adapter-based Approach for News Genre Classification 2023
NAP at SemEval-2023 Task 3: Is Less Really More? (Back-)Translation as Data Augmentation Strategies for Detecting Persuasion Techniques 2023