BabelBot at AraFinNLP2024: Fine-tuning T5 for Multi-dialect Intent Detection with Synthetic Data and Model Ensembling
Year: 2024
Type: conference-paper
Abstract: This paper presents our results for the Arabic Financial NLP (AraFinNLP) shared task at the Second Arabic Natural Language Processing Conference (ArabicNLP 2024).We participated in the first sub-task, Multi-dialect Intent Detection, which focused on cross-dialect intent detection in the banking domain.Our approach involved fine-tuning an encoder-only T5 model, generating synthetic data, and model ensembling.Additionally, we conducted an indepth analysis of the dataset, addressing annotation errors and problematic translations.Our model was ranked third in the shared task with an F1-score of 0.871.
Authors: Murhaf Fares, Samia Touileb
Institution: University of Bergen
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4402671432