Syntax Savants-UA at IberLEF 2024: Leveraging FLAN-T5-XXL for Automatic 5W1H Identification in Texts
Year: 2024
Type: conference-paper
Abstract: The 5W1H technique involves formulating six questions to briefly capture the basic information of a news text: What, When, Where, Who, Why, and How. This method enables an easy understanding of the news. This work, part of the FLARES competition at the Iberian Languages Evaluation Forum 2024, presents a Natural Language Processing (NLP) model, specifically FLAN-T5-XXL, designed to automatically extract 5W1H elements with an F1-score of 0.543. Our methodology includes creating task-specific templates and prompts and then fine-tuning the FLAN-T5-XXL model using annotated Spanish news articles provided by the competition organizers. We leverage Low-Rank Adaptation (LoRA) for efficient fine-tuning and conduct comprehensive hyperparameter optimization to enhance model performance and output generation. The evaluation demonstrates that FLAN-T5-XXL significantly outperforms other models. This study underscores the potential of Generative NLP models in automating information extraction from news texts.
Authors: Grande, Eduardo, Begga, Ahmed
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7015658975
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