Ishaan Shukla


2024

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PICT at StanceEval2024: Stance Detection in Arabic using Ensemble of Large Language Models
Ishaan Shukla | Ankit Vaidya | Geetanjali Kale
Proceedings of The Second Arabic Natural Language Processing Conference

This paper outlines our approach to the StanceEval 2024- Arabic Stance Evaluation shared task. The goal of the task was to identify the stance, one out of three (Favor, Against or None) towards tweets based on three topics, namely- COVID-19 Vaccine, Digital Transformation and Women Empowerment. Our approach consists of fine-tuning BERT-based models efficiently for both, Single-Task Learning as well as Multi-Task Learning, the details of which are discussed. Finally, an ensemble was implemented on the best-performing models to maximize overall performance. We achieved a macro F1 score of 78.02% in this shared task. Our codebase is available publicly.

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CLTeam1 at SemEval-2024 Task 10: Large Language Model based ensemble for Emotion Detection in Hinglish
Ankit Vaidya | Aditya Gokhale | Arnav Desai | Ishaan Shukla | Sheetal Sonawane
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

This paper outlines our approach for the ERC subtask of the SemEval 2024 EdiREF Shared Task. In this sub-task, an emotion had to be assigned to an utterance which was the part of a dialogue. The utterance had to be classified into one of the following classes- disgust, contempt, anger, neutral, joy, sadness, fear, surprise. Our proposed system makes use of an ensemble of language specific RoBERTA and BERT models to tackle the problem. A weighted F1-score of 44% was achieved by our system in this task. We conducted comprehensive ablations and suggested directions of future work. Our codebase is available publicly.