Intermediate linguistic task fine-tuning on Multi-lingual models

Rispoli, Luca (2022) Intermediate linguistic task fine-tuning on Multi-lingual models. [Laurea magistrale], Università di Bologna, Corso di Studio in Artificial intelligence [LM-DM270], Documento full-text non disponibile
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State-of-the-art NLP systems are generally based on the assumption that the underlying models are provided with vast datasets to train on. However, especially when working in multi-lingual contexts, datasets are often scarce, thus more research should be carried out in this field. This thesis investigates the benefits of introducing an additional training step when fine-tuning NLP models, named Intermediate Training, which could be exploited to augment the data used for the training phase. The Intermediate Training step is applied by training models on NLP tasks that are not strictly related to the target task, aiming to verify if the models are able to leverage the learned knowledge of such tasks. Furthermore, in order to better analyze the synergies between different categories of NLP tasks, experimentations have been extended also to Multi-Task Training, in which the model is trained on multiple tasks at the same time.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Rispoli, Luca
Relatore della tesi
Correlatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
Natural Language Processing,transformers,transfer learning,deep learning,multi-lingual,huggingface
Data di discussione della Tesi
20 Luglio 2022

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