Agnes Masip Gomez


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Enhancing Extreme Multi-Label Text Classification: Addressing Challenges in Model, Data, and Evaluation
Dan Li | Zi Long Zhu | Janneke van de Loo | Agnes Masip Gomez | Vikrant Yadav | Georgios Tsatsaronis | Zubair Afzal
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

Extreme multi-label text classification is a prevalent task in industry, but it frequently encounters challenges in terms of machine learning perspectives, including model limitations, data scarcity, and time-consuming evaluation. This paper aims to mitigate these issues by introducing novel approaches. Firstly, we propose a label ranking model as an alternative to the conventional SciBERT-based classification model, enabling efficient handling of large-scale labels and accommodating new labels. Secondly, we present an active learning-based pipeline that addresses the data scarcity of new labels during the update of a classification system. Finally, we introduce ChatGPT to assist with model evaluation. Our experiments demonstrate the effectiveness of these techniques in enhancing the extreme multi-label text classification task.